The rise of Generative AI (GenAI) brings about transformative potential across sectors, but its dual-use nature also amplifies risks. Governments globally are grappling with the challenge of regulating GenAI, balancing innovation against safety. China, the United States (US), and the European Union (EU) are at the forefront with initiatives like the Management of Algorithmic Recommendations, the Executive Order, and the AI Act, respectively. However, the rapid evolution of GenAI capabilities often outpaces the development of comprehensive safety measures, creating a gap between regulatory needs and technical advancements. A workshop co-organized by Google, University of Wisconsin, Madison (UW-Madison), and Stanford University aimed to bridge this gap between GenAI policy and technology. The diverse stakeholders of the GenAI space—from the public and governments to academia and industry—make any safety measures under consideration more complex, as both technical feasibility and regulatory guidance must be realized. This paper summarizes the discussions during the workshop which addressed questions, such as: How regulation can be designed without hindering technological progress? How technology can evolve to meet regulatory standards? The interplay between legislation and technology is a very vast topic, and we don’t claim that this paper is a comprehensive treatment on this topic. This paper is meant to capture findings based on the workshop, and hopefully, can guide discussion on this topic.

Money laundering is a serious financial crime where criminals aim to conceal the illegal source of their money via a series of transactions. Although banks have an obligation to monitor transactions, it is difficult to track these illicit money flows since they typically span over multiple banks, which cannot share this information due to privacy concerns. We present secure risk propagation, a novel efficient algorithm for money laundering detection across banks without violating privacy concerns. In this algorithm, each account is assigned a risk score, which is then propagated through the transaction network. In this article we present two results. Firstly, using data from a large Dutch bank, we show that it is possible to detect unusual activity using this model, with cash ratio as the risk score. With a recall of 20%, the precision improves from 15% to 40% by propagating the risk scores, reducing the number of false positives significantly. Secondly, we present a privacy-preserving solution for securely performing risk propagation over a joint, inter-bank transaction network. To achieve this, we use Secure Multi-Party Computation (MPC) techniques, which are particularly well-suited for the risk propagation algorithm due to its structural simplicity. We also show that the running time of this secure variant scales linearly in the amount of accounts and transactions. For 200, 000 transactions, two iterations of the secure algorithm between three virtual parties, run within three hours on a consumer-grade server.

Differential privacy (DP) is a key tool in privacy-preserving data analysis. Yet it remains challenging for non-privacy-experts to prove the DP of their algorithms. We propose a methodology for domain experts with limited data privacy background to empirically estimate the privacy of an arbitrary mechanism. Our Eureka moment is a new link---which we prove---between the problems of DP parameter-estimation and Bayes optimal classifiers in ML, which we believe can be of independent interest. Our estimator uses this link to achieve two desirable properties: (1) black-box, i.e., it does not require knowledge of the underlying mechanism, and (2) it has a theoretically-proven accuracy, depending on the underlying classifier used, allowing plug-and-play use of different classifiers. More concretely, motivated by the impossibility of the above task for unrestricted input domains (which we prove), we introduce a natural, application-inspired relaxation of DP which we term relative DP. Intuitively, relative DP defines a mechanism's privacy relative to an input set $T$, circumventing the above impossibility when $T$ is finite. Importantly, it preserves the key intuitive privacy guarantee of DP while enjoying a number of desirable DP properties---scalability, composition, and robustness to post-processing. We then devise a black-box poly-time $(\epsilon,\delta)$-relative DP estimator for any poly-size $T$---the first privacy estimator to support mechanisms with large output spaces while having tight accuracy bounds. As a result of independent interest, we generalize our theory to develop the first Distributional Differential Privacy (DDP) estimator. We benchmark our estimator in a proof-of-concept implementation. First, using kNN as the classifier we show that our method (1) produces a tight, analytically computed $(\epsilon, \delta)$-DP trade-off of low-dimensional Laplace and Gaussian mechanisms---the first to do so, (2) accurately estimates the privacy spectrum of DDP mechanisms, and (3) can verify a DP mechanism's implementations, e.g., Sparse Vector Technique, Noisy Histogram, and Noisy max. Our implementation and experiments demonstrate the potential of our framework, and highlight its computational bottlenecks in estimating DP, e.g., in terms of the size of $\delta$ and the data dimensionality. Our second, neural-network-based instantiation makes a first step in showing that our method can be extended to mechanisms with high-dimensional outputs.

Arma is a Byzantine Fault Tolerant (BFT) consensus system designed to achieve horizontal scalability across all hardware resources: network bandwidth, CPU, and disk I/O. As opposed to preceding BFT protocols, Arma separates the dissemination and validation of client transactions from the consensus process, restricting the latter to totally ordering only metadata of batches of transactions. This separation enables each party to distribute compute and storage resources for transaction validation, dissemination and disk I/O among multiple machines, resulting in horizontal scalability. Additionally, Arma ensures censorship resistance by imposing a maximum time limit on the inclusion of client transactions. We built and evaluated two Arma prototypes. The first is an independent system handling over 200,000 transactions per second, the second integrated into Hyperledger Fabric, speeding its consensus by an order of magnitude.

The decentralized finance (DeFi) ecosystem has proven to be popular in facilitating financial operations, such as token exchange and lending. The public availability of DeFi platforms’ code, together with real-time data on all user interactions with them, has given rise to complex tools that find and seize profit opportunities on behalf of users. In this work, we show that both users and the aforementioned tools sometimes act suboptimally: their profits can be increased by more than 100%, with the highest amount of missed revenue by a suboptimal action reaching 428.14ETH ($517K). To reach these findings, we examine core DeFi primitives used by over 850 platforms that are responsible for a daily volume of more than 100 million USD in Ethereum alone: (1) lending and borrowing funds, (2) using flashswaps to close arbitrage opportunities between decentralized exchanges (DEXs), (3) liquidation of insolvent loans using flashswaps, which combines the previous two. The profit that can be made from each primitive is then cast as an optimization problem that can be solved. We show that missed opportunities to make a profit are noticed by others and are sometimes followed by back-running transactions that extract profits using similar actions. By analyzing these events, we find that some transactions are circumstantially tied to specific miners and hypothesize that they use their knowledge of private orderflow for a profit. Essentially, this is an instance of miner-extractable value (MEV) "in action".

Analyzing user data while protecting the privacy of individuals remains a big challenge. Trusted execution environments (TEEs) are a possible solution as they protect processes and Virtual Machines (VMs) against malicious hosts. However, TEEs can leak access patterns to code and to the data being processed. Furthermore, when data is stored in a TEE database, the data volume required to answer a query is another unwanted side channel that contains sensitive information. Both types of information leaks, access patterns and volume patterns, allow for database reconstruction attacks. In this paper, we present Menhir, an oblivious TEE database that hides access patterns with ORAM guarantees and volume patterns through differential privacy. The database allows range and point queries with SQL-like WHERE-clauses. It builds on the state-of-the-art oblivious AVL tree construction Oblix (S&P'18), which by itself does not protect against volume leakage. We show how volume leakage can be exploited in range queries and improve the construction to mitigate this type of attack. We prove the correctness and obliviousness of Menhir. Our evaluation shows that our approach is feasible and scales well with the number of rows and columns in the database.

We present a novel protocol for issuing and transferring tokens across blockchains without the need of a trusted third party or cross-chain bridge. In our scheme, the blockchain is used for double-spend protection only, while the authorisation of token transfers is performed off-chain. Due to the universality of our approach, it works in almost all blockchain settings. It can be implemented immediately on UTXO blockchains such as Bitcoin without modification, and on account-based blockchains such as Ethereum by introducing a smart contract that mimics the properties of a UTXO. We provide a proof-of-concept implementation of an NFT that is issued on Bitcoin, transferred to Ethereum, and then transferred back to Bitcoin. Our new approach means that users no longer need to be locked into one blockchain when issuing and transferring tokens.

Counter-strategies are key components of coercion-resistant voting schemes, allowing voters to submit votes that represent their own intentions in an environment controlled by a coercer. By deploying a counter-strategy a voter can prevent the coercer from learning if the voter followed the coercer’s instructions or not. Two effective counter-strategies have been proposed in the literature, one based on fake credentials and another on revoting. While fake-credential schemes assume that voters hide cryptographic keys away from the coercer, revoting schemes assume that voters can revote after being coerced. In this work, we present a new counter-strategy technique that enables flexible vote updating, that is, a revoting approach that provides protection against coercion even if the adversary is able to coerce a voter at the very last minute of the voting phase. We demonstrate that our technique is effective by implementing it in Loki, an Internet-based coercion-resistant voting scheme that allows revoting. We prove that Loki satisfies a game-based definition of coercion-resistance that accounts for flexible vote updating. To the best of our knowledge, we provide the first technique that enables deniable coercion- resistant voting and that can evade last-minute voter coercion.

In cryptocurrencies, all transactions are public. For their adoption, it is important that these transactions, while publicly verifiable, do not leak information about the identity and the balances of the transactors. For UTXO-based cryptocurrencies, there are well-established approaches (e.g., ZCash) that guarantee full privacy to the transactors. Full privacy in UTXO means that each transaction is anonymous within the set of all private transactions ever posted on the blockchain. In contrast, for account-based cryptocurrencies (e.g., Ethereum) full privacy, that is, privacy within the set of all accounts, seems to be impossible to achieve within the constraints of blockchain transactions (e.g., they have to fit in a block). Indeed, every approach proposed in the literature achieves only a much weaker privacy guarantee called $k-$anonymity where a transactor is private within a set of $k$ account holders. $k-$anonymity is achieved by adding $k$ accounts to the transaction, which concretely limits the anonymity guarantee to a very small constant (e.g., $~$64 for QuisQuis and $~$256 for anonymous Zether), compared to the set of all possible accounts. In this paper, we propose a completely new approach that does not achieve anonymity by including more accounts in the transaction, but instead makes the transaction itself ``smarter''. Our key contribution is to provide a mechanism whereby a compact transaction can be used to correctly update all accounts. Intuitively, this guarantees that all accounts are equally likely to be the recipients/sender of such a transaction. We, therefore, provide the first protocol that guarantees full privacy in account-based cryptocurrencies PriFHEte The contribution of this paper is theoretical. Our main objective is to demonstrate that achieving full privacy in account-based cryptocurrency is actually possible. We see our work as opening the door to new possibilities for anonymous account-based cryptocurrencies. Nonetheless, in this paper, we also discuss PriFHEte's potential to be developed in practice by leveraging the power of off-chain scalability solutions such as zk rollups.

A ranking function for permutations maps every permutation of length $n$ to a unique integer between $0$ and $n!-1$. For permutations of size that are of interest in cryptographic applications, evaluating such a function requires multiple-precision arithmetic. This work introduces a quasi-optimal ranking technique that allows us to rank a permutation efficiently without needing a multiple-precision arithmetic library. We present experiments that show the computational advantage of our method compared to the standard lexicographic optimal permutation ranking. As an application of our result, we show how this technique improves the signature sizes and the efficiency of PERK digital signature scheme.

We propose Challenger a peer-to-peer blockchain-based middleware architecture for narrative games, and discuss its resilience to cheating attacks. Our architecture orchestrates nine services in a fully decentralized manner where nodes are not aware of the entire composition of the system nor its size. All these components are orchestrated together to obtain (strong) resilience to cheaters. The main contribution of the paper is to provide, for the first time, an architecture for narrative games agnostic of a particular blockchain that brings together several distinct research areas, namely distributed ledgers, peer-to-peer networks, multi-player-online games and resilience to attacks.

Enhancing privacy on smart contract-enabled blockchains has garnered much attention in recent research. Zero-knowledge proofs (ZKPs) is one of the most popular approaches, however, they fail to provide full expressiveness and fine-grained privacy. To illustrate this, we underscore an underexplored type of Miner Extractable Value (MEV), called Residual Bids Extractable Value (RBEV). Residual bids highlight the vulnerability where unfulfilled bids inadvertently reveal traders’ unmet demands and prospective trading strategies, thus exposing them to exploitation. ZKP-based approaches failed to ad- dress RBEV as they cannot provide post-execution privacy without some level of information disclosure. Other MEV mitigations like fair-ordering protocols also failed to address RBEV. We introduce Ratel, an innovative framework bridging a multi-party computation (MPC) prototyping framework (MP-SPDZ) and a smart contract language (Solidity), harmonizing the privacy with full expressiveness of MPC with Solidity ’s on-chain programmability. This synergy empowers developers to effortlessly craft privacy-preserving decentralized applications (DApps). We demonstrate Ratel’s efficacy through two distinguished decentralized finance (DeFi) applications: a decentralized exchange and a collateral auction, effectively mitigating the potential RBEV issue. Furthermore, Ratel is equipped with a lightweight crash-reset mechanism, enabling the seamless recovery of transiently benign faulty nodes. To prevent the crash-reset mechanism abused by malicious entities and ward off DoS attacks, we incorporate a cost-utility analysis anchored in the Bayesian approach. Our performance evaluation of the applications developed under the Ratel framework underscores their competency in managing real-world peak-time workloads.

Fully Homomorphic Encryption (FHE) is a transformative technology that enables computations on encrypted data without requiring decryption, promising enhanced data privacy. However, its adoption has been limited due to significant performance overheads. Recent advances include the proposal of domain-specific, highly-parallel hardware accelerators designed to overcome these limitations. This paper introduces PICA, a comprehensive compiler framework designed to simplify the programming of these specialized FHE accelerators and integration with existing FHE libraries. PICA leverages a novel polynomial Instruction Set Architecture (p-ISA), which abstracts polynomial rings and their arithmetic operations, serving as a fundamental data type for the creation of compact, efficient code embracing high-level operations on polynomial rings, referred to as kernels, e.g., encompassing FHE primitives like arithmetic and ciphertext management. We detail a kernel generation framework that translates high-level FHE operations into pseudo-code using p-ISA, and a subsequent tracing framework that incorporates p-ISA functionalities and kernels into established FHE libraries. Additionally, we introduce a mapper to coordinate multiple FHE kernels for optimal application performance on targeted hardware accelerators. Our evaluations demonstrate PICA's efficacy in creation of compact and efficient code, when compared with an x64 architecture. Particularly in managing complex FHE operations such as relinearization, where we observe a 25.24x instruction count reduction even when a large batch size (8192) is taken into account.

We study selfish mining attacks in longest-chain blockchains like Bitcoin, but where the proof of work is replaced with efficient proof systems -- like proofs of stake or proofs of space -- and consider the problem of computing an optimal selfish mining attack which maximizes expected relative revenue of the adversary, thus minimizing the chain quality. To this end, we propose a novel selfish mining attack that aims to maximize this objective and formally model the attack as a Markov decision process (MDP). We then present a formal analysis procedure which computes an $\epsilon$-tight lower bound on the optimal expected relative revenue in the MDP and a strategy that achieves this $\epsilon$-tight lower bound, where $\epsilon>0$ may be any specified precision. Our analysis is fully automated and provides formal guarantees on the correctness. We evaluate our selfish mining attack and observe that it achieves superior expected relative revenue compared to two considered baselines. In concurrent work [Sarenche FC'24] does an automated analysis on selfish mining in predictable longest-chain blockchains based on efficient proof systems. Predictable means the randomness for the challenges is fixed for many blocks (as used e.g., in Ouroboros), while we consider unpredictable (Bitcoin-like) chains where the challenge is derived from the previous block.

In recent years, cloud vendors have started to supply paid services for data analysis by providing interfaces of their well-trained neural network models. However, customers lack tools to verify whether outcomes supplied by cloud vendors are correct inferences from particular models, in the face of lazy or malicious vendors. The cryptographic primitive called zero-knowledge proof (ZKP) addresses this problem. It enables the outcomes to be verifiable without leaking information about the models. Unfortunately, existing ZKP schemes for neural networks have high computational overheads, especially for the non-linear layers in neural networks. In this paper, we propose an efficient and extensible ZKP framework for neural networks. Our work improves the performance of the proofs for non-linear layers. Compared to previous works relying on the technology of bit decomposition, we convert complex non-linear relations into range and exponent relations, which significantly reduces the number of constraints required to prove non-linear layers. Moreover, we adopt a modular design to make our framework compatible with more neural networks. Specifically, we propose two enhanced range and lookup proofs as basic blocks. They are efficient in proving the satisfaction of range and exponent relations. Then, we constrain the correct calculation of primitive non-linear operations using a small number of range and exponent relations. Finally, we build our ZKP framework from the primitive operations to the entire neural networks, offering the flexibility for expansion to various neural networks. We implement our ZKPs for convolutional and transformer neural networks. The evaluation results show that our work achieves over $168.6\times$ (up to $477.2\times$) speedup for separated non-linear layers and $41.4\times$ speedup for the entire ResNet-101 convolutional neural network, when compared with the state-of-the-art work, Mystique. In addition, our work can prove GPT-2, a transformer neural network with $117$ million parameters, in $287.1$ seconds, achieving $35.7\times$ speedup over ZKML, which is a state-of-the-art work supporting transformer neural networks.

The TFHE cryptosystem only supports small plaintext space, up to 5 bits with usual parameters. However, one solution to circumvent this limitation is to decompose input messages into a basis B over multiple ciphertexts. In this work, we introduce B-gates, an extension of logic gates to non binary bases, to compute base B logic circuit. The flexibility introduced by our approach improves the speed performance over previous approaches such as the so called tree-based method which requires an exponential amount of operations in the number of inputs. We provide experimental results using sorting as a benchmark application and, additionally, we obtain a speed-up of ×3 in latency compared to state of the art BGV techniques for this application. As an additional result, we introduce a keyswitching key specific to packing TLWE ciphertexts into TRLWE ciphertexts with redundancy, which is of interest in many functional bootstrapping scenarios.

This paper studies the optimal transaction fee mechanisms for blockchains, focusing on the distinction between price-based ($\mathcal{P}$) and quantity-based ($\mathcal{Q}$) controls. By analyzing factors such as demand uncertainty, validator costs, cryptocurrency price fluctuations, price elasticity of demand, and levels of decentralization, we establish criteria that determine the selection of transaction fee mechanisms. We present a model framed around a Nash bargaining game, exploring how blockchain designers and validators negotiate fee structures to balance network welfare with profitability. Our findings suggest that the choice between $\mathcal{P}$ and $\mathcal{Q}$ mechanisms depends critically on the blockchain’s specific technical and economic features. The study concludes that no single mechanism suits all contexts and highlights the potential for hybrid approaches that adaptively combine features of both $\mathcal{P}$ and $\mathcal{Q}$ to meet varying demands and market conditions.

Machine learning is an important tool for analyzing large data sets, but its use on sensitive data may be limited by regulation. One solution to this problem is to perform machine learning tasks on encrypted data using homomorphic encryption, which enables arbitrary computation on encrypted data. We take a fresh look at one specific task: training a logistic regression model on encrypted data. The most important factor in the efficiency of a solution is the multiplicative depth of the homomorphic circuit. Two prior works have given circuits with multiplicative depth of five per training iteration. We optimize one of these solutions, by Han et al. [Han+18], and give a circuit with half the multiplicative depth per iteration on average, which allows us to perform twice as many training iterations in the same amount of time. In the process of improving the state-of-the-art circuit for this task, we identify general techniques to improve homomorphic circuit design for two broad classes of algorithms: iterative algorithms, and algorithms based on linear algebra over real numbers. First, we formalize the encoding scheme from [Han+18] for encoding linear algebra objects as plaintexts in the CKKS homomorphic encryption scheme. We also show how to use this encoding to homomorphically compute many basic linear algebra operations, including novel operations not discussed in prior work. This “toolkit” is generic, and can be used in any application based on linear algebra. Second, we demonstrate how generic compiler techniques for loop optimization can be used to reduce the multiplicative depth of iterative algorithms.

We present mining-based techniques to reduce the size of various cryptographic outputs without loss of security. Our approach can be generalized for multiple primitives, such as cryptographic key generation, signing, hashing and encryption schemes, by introducing a brute-forcing step to provers/senders aiming at compressing submitted cryptographic material. Interestingly, mining can result in record-size cryptographic outputs, and we show that 5%-12% shorter hash digests and signatures are practically feasible even with commodity hardware. As a result, our techniques make compressing addresses and transaction signatures possible in order to pay less fees in blockchain applications while decreasing the demand for blockchain space, a major bottleneck for initial syncing, communication and storage. Also, the effects of "compressing once - then reuse'' at mass scale can be economically profitable in the long run for both the Web2 and Web3 ecosystems. Our paradigm relies on a brute-force search operation in order to craft the primitive's output such that it fits into fewer bytes, while the "missing" fixed bytes are implied by the system parameters and omitted from the actual communication. While such compression requires computational effort depending on the level of compression, this cost is only paid at the source (i.e. in blockchains, senders are rewarded by lowered transaction fees), and the benefits of the compression are enjoyed by the whole ecosystem. As a starting point, we show how our paradigm applies to some basic primitives commonly used in blockchain applications but also traditional Web2 transactions (such as shorter digital certificates), and show how security is preserved using a bit security framework. Surprisingly, we also identified cases where wise mining strategies require proportionally less effort than naive brute-forcing, shorter hash-based signatures being one of the best examples. We also evaluate our approach for several primitives based on different levels of compression. Our evaluation concretely demonstrates the benefits both in terms of financial cost and storage if adopted by the community, and we showcase how our technique can achieve up to 83.21% reduction in smart contract gas fees at a cost of less than 4 seconds of computation on a single core.

Topic modelling refers to a popular set of techniques used to discover hidden topics that occur in a collection of documents. These topics can, for example, be used to categorize documents or label text for further processing. One popular topic modelling technique is Latent Dirichlet Allocation (LDA). In topic modelling scenarios, the documents are often assumed to be in one, centralized dataset. However, sometimes documents are held by different parties, and contain privacy- or commercially-sensitive information that cannot be shared. We present a novel, decentralized approach to train an LDA model securely without having to share any information about the content of the documents with the other parties. We preserve the privacy of the individual parties using a combination of privacy enhancing technologies. We show that our decentralized, privacy preserving LDA solution has a similar accuracy compared to an (insecure) centralised approach. With $1024$-bit Paillier keys, a topic model with $5$ topics and $3000$ words can be trained in around $16$ hours. Furthermore, we show that the solution scales linearly in the total number of words and the number of topics.

Storage of sensitive multi-dimensional arrays must be secure and efficient in storage and processing time. Searchable encryption allows one to trade between security and efficiency. Searchable encryption design focuses on building indexes, overlooking the crucial aspect of record retrieval. Gui et al. (PoPETS 2023) showed that understanding the security and efficiency of record retrieval is critical to understand the overall system. A common technique for improving security is partitioning data tuples into parts. When a tuple is requested, the entire relevant part is retrieved, hiding the tuple of interest. This work assesses tuple partitioning strategies in the dense data setting, considering parts that are random, $1$-dimensional, and multi-dimensional. We consider synthetic datasets of $2$, $3$ and $4$ dimensions, with sizes extending up to $2$M tuples. We compare security and efficiency across a variety of record retrieval methods. Our findings are: 1. For most configurations, multi-dimensional partitioning yields better efficiency and less leakage. 2. 1-dimensional partitioning outperforms multi-dimensional partitioning when the first (indexed) dimension is any size as long as the query is large in all other dimensions except the (the first dimension can be any size). 3. The leakage of 1-dimensional partitioning is reduced the most when using a bucketed ORAM (Demertiz et al., USENIX Security 2020).

Fully Homomorphic Encryption (FHE) is a powerful Privacy-Enhancing Technology (PET) that enables computations on encrypted data without having access to the secret key. While FHE holds immense potential for enhancing data privacy and security, creating its practical applications is associated with many difficulties. A significant barrier is the absence of easy-to-use, standardized components that developers can utilize as foundational building blocks. Addressing this gap requires constructing a comprehensive library of FHE components, a complex endeavor due to multiple inherent problems. We propose a competition-based approach for building such a library. More concretely, we present FHERMA, a new challenge platform that introduces black-box and white-box challenges, and fully automated evaluation of submitted FHE solutions. The initial challenges on the FHERMA platform are motivated by practical problems in machine learning and blockchain. The winning solutions get integrated into an open-source library of FHE components, which is available to all members of the PETs community under the Apache 2.0 license.

Anonymous Zether, proposed by Bunz et al. (FC, 2020) and subsequently improved by Diamond (IEEE S&P, 2021) is an account-based confidential payment mechanism that works by using a smart contract to achieve privacy (i.e. identity of receivers to transactions and payloads are hidden). In this work, we look at simplifying the existing protocol while also achieving batching of transactions for multiple receivers, while ensuring consensus and forward secrecy. To the best of our knowledge, this work is the first to formally study the notion of forward secrecy in the setting of blockchain, borrowing a very popular and useful idea from the world of secure messaging. Specifically, we introduce: - FUL-Zether, a forward-secure version of Zether (Bunz et al., FC, 2020). - PRIvate DEcentralized Confidental Transactions (PriDe CT), a much-simplified version of Anonymous Zether that achieves competitive performance and enables batching of transactions for multiple receivers. - PRIvate DEcentralized Forward-secure Until Last update Confidential Transactions (PriDeFUL CT), a forward-secure version of PriDe CT. We also present an open-source, Ethereum-based implementation of our system. PriDe CT uses linear homomorphic encryption as Anonymous Zether but with simpler zero-knowledge proofs. PriDeFUL CT uses an updatable public key encryption scheme to achieve forward secrecy by introducing a new DDH-based construction in the standard model. In terms of transaction sizes, Quisquis (Asiacrypt, 2019), which is the only cryptocurrency that supports batchability (albeit in the UTXO model), has 15 times more group elements than PriDe CT. Meanwhile, for a ring of $N$ receivers, Anonymous Zether requires $6\log N$ more terms even without accounting for the ability to batch in PriDe CT. Further, our implementation indicates that, for $N=32$, even if there were 7 intended receivers, PriDe CT outperforms Anonymous Zether in proving time and gas consumption.

We construct a digital signature scheme for images that allows the image to be compressed without invalidating the signature. More specifically, given a JPEG image signed with our signature scheme, a third party can compress the image using JPEG compression, and, as long as the quantization tables only include powers of two, derive a valid signature for the compressed image, without access to the secret signing key, and without interaction with the signer. Our scheme is constructed using a standard digital signature scheme and a hash function as building blocks. This form of signatures that allow image compression could be useful in mitigating some of the threats posed by generative AI and fake news, without interfering with all uses of generative AI. Taking inspiration from related signature schemes, we define a notion of unforgeability and prove our construction to be secure. Additionally, we show that our signatures have size 32.5 kb under standard parameter choices. Using image quality assessment metrics, we show that JPEG compression with parameters as specified by our scheme, does not result in perceivably reduced visual fidelity, compared to standard JPEG compression.

The Swiss law on electronic identity (LSIE) was rejected on March 7, 2021. Its opponents accused it of involving private companies which could thus collect citizens' data and store them centrally. Six motions with identical wording were tabled on March 10, 2021: they all ask the Swiss Federal Council to set up a state-run system allowing citizens to prove their identity online in complete confidence. They stipulate that only necessary information is collected and stored in a decentralized manner. The Swiss Federal Council has recommended to Parliament to approve these motions on May 26, 2021, and wishes to propose a new e-ID solution responding to citizens' concerns as soon as possible. The Federal Department of Justice and Police has been asked to draw up a first draft presenting several technical solutions and specifying their respective costs. Following the publication of a working document on September 2, 2021, a public consultation was opened. It ended on October 14, 2021, with a public debate organized at the Government House in Bern and broadcasted live on a virtual platform. Self-Sovereign Identity (SSI) is one of the solutions identified during this process. It gives the citizens control of their electronic identity: they hold credentials issued by public administrations and choose the data they wish to disclose when they authenticate with a service (they can for example prove that they are over 18 without specifying their exact date of birth). We propose here a decentralized and user-centric e-ID system based on SSI principles. Our solution embraces an open-source philosophy, fostering transparency and community involvement. We employ blockchain technology as a design pattern to establish trust and ensure the immutability of identity-related data. By design, our solution ensures the right to be forgotten by exclusively storing the digests of verifiable credentials on the blockchain. To demonstrate the feasibility and effectiveness of our SSI solution, we have developed a proof of concept leveraging the Partisia blockchain.

In smart contract blockchain platforms such as Ethereum, users interact with the system by issuing transactions. System operators called miners or validators add those transactions to the blockchain. Users attach to each transaction a fee, which is collected by the miner who placed it in the blockchain. Miners naturally prioritize better-paying transactions. This process creates a volatile fee market due to limited throughput and fluctuating demand. The fee required to place a transaction in the future is unknown; yet, ensuring timely transaction confirmation is critical for securing smart contracts that represent billions of dollars and underpin prominent blockchain scaling solutions. We present LedgerHedger, a novel mechanism that guarantees the confirmation of a transaction within a specified time frame. Due to the absence of external enforcement in decentralized systems, LedgerHedger uses incentives. Its core is a hedging agreement between a transaction issuer and a second party, possibly a miner. The issuing party pays for the transaction upfront while the second party commits to paying any necessary fees when the transaction is issued in the future, even if they exceed the original payment. LedgerHedger gives rise to a strategic game, where the issuing party deposits the transaction payment and the committing party deposits a collateral. During the target time frame, the latter is required to confirm the transaction if it exists, or they have the option to withdraw the payment and the collateral if the transaction is not presented. We demonstrate that for a broad range of parameters, a subgame perfect equilibrium exists where both parties are incentivized to act as desired, thereby guaranteeing transaction confirmation. We implement LedgerHedger and deploy it on an Ethereum test network, showcasing its efficacy and minor overhead.

Searchable Symmetric Encryption (SSE) has opened up an attractive avenue for privacy-preserved processing of outsourced data on the untrusted cloud infrastructure. SSE aims to support efficient Boolean query processing with optimal storage and search overhead over large real databases. However, current constructions in the literature lack the support for multi-client search and dynamic updates to the encrypted databases, which are essential requirements for the widespread deployment of SSE on real cloud infrastructures. Trivially extending a state-of-the-art single client dynamic construction, such as ODXT (Patranabis et al., NDSS’21), incurs significant leakage that renders such extension insecure in practice. Currently, no SSE construction in the literature offers efficient multi-client query processing and search with dynamic updates over large real databases while maintaining a benign leakage profile. This work presents the first dynamic multi-client SSE scheme Nomos supporting efficient multi-client conjunctive Boolean queries over an encrypted database. Precisely, Nomos is a multi-reader-single-writer construction that allows only the gate-keeper (or the data-owner) - a trusted entity in the Nomos framework, to update the encrypted database stored on the adversarial server. Nomos achieves forward and type-II backward privacy of dynamic SSE constructions while incurring lesser leakage than the trivial extension of ODXT to a multi- client setting. Furthermore, our construction is practically efficient and scalable - attaining linear encrypted storage and sublinear search overhead for conjunctive Boolean queries. We provide an experimental evaluation of software implementation over an extensive real dataset containing millions of records. The results show that Nomos performance is comparable to the state-of-the-art static conjunctive SSE schemes in practice.

Homomorphic encryption (HE) allows for computations over ciphertexts while they are encrypted. Because of this, HE supports the outsourcing of computation on private data. Due to the additional risks caused by data outsourcing, the ability to recover from losses is essential, but doing so on data encrypted under an HE scheme introduces additional challenges for recovery and usability. This work introduces X-Cipher, which aims to make HE ciphertexts resilient by ensuring they are private and recoverable simultaneously at all stages during data outsourcing. X-Cipher allows data recovery without requiring the decryption of HE ciphertexts and maintains its ability to recover and keep data private when a cluster server has been compromised. X-Cipher allows for reduced ciphertext storage overhead by introducing novel encoding and leveraging previously introduced ciphertext packing. X-Cipher's capabilities were evaluated on a synthetic dataset to demonstrate that X-Cipher enables secure availability capabilities while enabling privacy-preserving outsourced computations.

This paper presents an in-depth exploration of the development and deployment of a Layer 1 (L1) blockchain designed to underpin metaverse experiences. As the digital and physical realms become increasingly intertwined, the metaverse emerges as a frontier for innovation, demanding robust, scalable, and secure infrastructure. The core of our investigation centers around the challenges and insights gained from constructing a blockchain framework capable of supporting the vast, dynamic environments of the metaverse. Through the development process, we identified key areas of focus: interoperability, performance and scalability, cost, identity, privacy, security, and accessibility. Our findings indicate that most challenges can be effectively addressed through the implementation of cryptography and subnets (i.e., Avalanche architecture), which allow for segmented, optimized environments within the broader metaverse ecosystem. This approach not only enhances performance but also provides a flexible framework for managing the diverse needs of metaverse applications.

We reflect on our experiences analysing cryptography deployed “in the wild” and give recommendations to fellow researchers about this process.

To enable parallel processing, the Directed Acyclic Graph (DAG) structure is introduced to the design of asynchronous Byzantine Fault Tolerant (BFT) consensus protocols, known as DAG-based BFT. Existing DAG-based BFT protocols operate in successive waves, with each wave containing three or four Reliable Broadcast (RBC) rounds to broadcast data, resulting in high latency due to the three communication steps required in each RBC. For instance, Tusk, a state-of-the-art DAG-based BFT protocol, has a good-case latency of 7 communication steps and an expected worst latency of 21 communication steps. To reduce latency, we propose GradedDAG, a new DAG-based BFT consensus protocol based on our adapted RBC called Graded RBC (GRBC) and the Consistent Broadcast (CBC), with each wave consisting of only one GRBC round and one CBC round. Through GRBC, a replica can deliver data with a grade of 1 or 2, and a non-faulty replica delivering the data with grade 2 can ensure that more than 2/3 of replicas have delivered the same data. Meanwhile, through CBC, data delivered by different non-faulty replicas must be identical. In each wave, a block in the GRBC round will be elected as the leader. If a leader block has been delivered with grade 2, it and all its ancestor blocks can be committed. GradedDAG offers a good-case latency of 4 communication steps and an expected worst latency of 7.5 communication steps, significantly lower than the state-of-theart. Experimental results demonstrate GradedDAG’s feasibility and efficiency.

Nowadays Federated learning (FL) is established as one of the best techniques for collaborative machine learning. It allows a set of clients to train a common model without disclosing their sensitive and private dataset to a coordination server. The latter is in charge of the model aggregation. However, FL faces some problems, regarding the security of updates, integrity of computation and the availability of a server. In this paper, we combine some new ideas like clients’ reputation with techniques like secure aggregation using Homomorphic Encryption and verifiable secret sharing using Multi-Party Computation techniques to design a decentralized FL system that addresses the issues of incentives, security and availability amongst others. One of the original contributions of this work is the new leader election protocol which uses a secure shuffling and is based on a proof of reputation. Indeed, we propose to select an aggregator among the clients participating to the FL training using their reputations. That is, we estimate the reputation of each client at every FL iteration and then we select the next round aggregator from the set of clients with the best reputations. As such, we remove misbehaving clients (e.g., byzantines) from the list of clients eligible for the role of aggregation server.

In [1], two generic constructions for biometric-based non-transferable Attribute Based Credentials (biometric ABC) are presented, which offer different trade-offs between efficiency and trust assumptions. In this paper, we focus on the second scheme denoted as BioABC-ZK that tries to remove the strong (and unrealistic) trust assumption on the Reader R, and show that BioABC-ZK has a security flaw for a colluding R and Verifier V. Besides, BioABC-ZK lacks GDPR-compliance, which requires secure processing of biometrics, for instance in form of Fuzzy Extractors, as opposed to (i) storing the reference biometric template aBio in the user's mobile phone and (ii) processing of biometrics using an external untrusted R, whose foreign manufacturers are unlikely to adjust their products according to GDPR. The contributions of this paper are threefold. First, we review efficient biometric ABC schemes to identify the privacy-by-design criteria for them. In view of these principles, we propose a new architecture for biometric ABC of [2] by adapting the recently introduced core/helper setting of [3]. Briefly, a user in our modified setting is composed of a constrained core device (a SIM card) inside a helper device (a smart phone with dual SIM and face recognition feature), which -as opposed to [1]- does not need to store aBio. This way, the new design provides Identity Privacy without the need for an external R and/or a dedicated hardware per user such as a biometric smart card reader or a tamper proof smart card as in current hardware-bound credential systems. Besides, the new system maintains minimal hardware requirements on the SIM card -only responsible for storing ABC and helper data-, which results in easy adoption and usability without loosing efficiency, if recently introduced key derivation scheme of [4] and the modified ABC scheme of [2] are employed together. As a result, a total overhead of 500 milliseconds to a showing of a comparable non-biometric ABC is obtained instead of the 2.1 seconds in [1] apart from the removal of computationally expensive pairings. Finally, as different from [1], auditing is achieved via Blockchain instead of proving in zero-knowledge the actual biometric matching by the user to reveal malicious behavior of R and V.

In the common random string model, the parties executing a protocol have access to a uniformly random bit string. It is known that under standard intractability assumptions, we can realize any ideal functionality with universally composable (UC) security if a trusted common random string (CrS) setup is available. It was always a question of where this CrS should come from since the parties provably could not compute it themselves. Trust assumptions are required, so minimizing the level of such trust is a fundamentally important task. Our goal is to design a CrS setup protocol under a weakened trust assumption. We present an HW-token-based CrS setup for 2-party cryptographic protocols using a single token only. Our protocol is a UC-secure realization of ideal common random string functionality FCrS. We show the multiple-session security of the protocol and we also consider the multi-party extension of it.

Public-key cryptography is widely deployed for encrypting stored files. This paper uses microbenchmarks and purchase costs to predict the performance of various post-quantum KEMs in this application. In particular, this paper concludes that Classic McEliece is (1) the most efficient option and (2) easily affordable. As a quantitative example, the estimated five-year per-user cost of mceliece6960119f is 0.00024 dollars if 100000 files are encrypted and stored for an average user during those five years and each file is decrypted twice on average.

Homomorphic encryption (HE) enables computation delegation to untrusted third parties while maintaining data confidentiality. Hybrid encryption (a.k.a transciphering) allows a reduction in the number of ciphertexts and storage size, which makes FHE solutions practical for a variety of modern applications. Still, modern transciphering has three main drawbacks: 1) lack of standardization or bad performance of symmetric decryption under FHE; 2) post-HE-evaluation is limited to small-size applications; 3) lack of input data integrity. Interestingly, modern-size secure inference applications were demonstrated using approximated FHE schemes such as CKKS. However, implementing transciphering using standard Authenticated Encryption (AE) over CKKS is challenging due to its approximated nature. In this paper, we aim to close these gaps. First, we report and demonstrate the first end-to-end process that uses transciphering for real-world applications i.e., running deep neural network (DNN) inference under encryption. For that, we discuss the concept of Authenticated Transciphering (AT), which like AE, provides some integrity guarantees for the transciphered data. Finally, to demonstrate the AT concept, we report on the first implementation of Ascon decryption under CKKS, and complete the picture with a detailed technical description of our AES-GCM implementation under CKKS.

Banks publish daily a list of available securities/assets (axe list) to selected clients to help them effectively locate Long (buy) or Short (sell) trades at reduced financing rates. This reduces costs for the bank, as the list aggregates the bank's internal firm inventory per asset for all clients of long as well as short trades. However, this is somewhat problematic: (1) the bank's inventory is revealed; (2) trades of clients who contribute to the aggregated list, particularly those deemed large, are revealed to other clients. Clients conducting sizable trades with the bank and possessing a portion of the aggregated asset exceeding $50\%$ are considered to be concentrated clients. This could potentially reveal a trading concentrated client's activity to their competitors, thus providing an unfair advantage over the market. Atlas-X Axe Obfuscation, powered by new differential private methods, enables a bank to obfuscate its published axe list on a daily basis while under continual observation, thus maintaining an acceptable inventory Profit and Loss (P\&L) cost pertaining to the noisy obfuscated axe list while reducing the clients' trading activity leakage. Our main differential private innovation is a differential private aggregator for streams (time series data) of both positive and negative integers under continual observation. For the last two years, Atlas-X system has been live in production across three major regions—USA, Europe, and Asia—at J.P. Morgan, a major financial institution, facilitating significant profitability. To our knowledge, it is the first differential privacy solution to be deployed in the financial sector. We also report benchmarks of our algorithm based on (anonymous) real and synthetic data to showcase the quality of our obfuscation and its success in production.

Classifying images has become a straightforward and accessible task, thanks to the advent of Deep Neural Networks. Nevertheless, not much attention is given to the privacy concerns associated with sensitive data contained in images. In this study, we propose a solution to this issue by exploring an intersection between Machine Learning and cryptography. In particular, Fully Homomorphic Encryption (FHE) emerges as a promising solution, as it enables computations to be performed on encrypted data. We, therefore, propose a Residual Network implementation based on FHE which allows the classification of encrypted images, ensuring that only the user can see the result. We suggest a circuit which reduces the memory requirements by more than 85% compared to the most recent works, while maintaining a high level of accuracy and a short computational time. We implement the circuit using the well-known CKKS scheme, which enables approximate encrypted computations. We evaluate the results from three perspectives: memory requirements, computational time and calculations precision. We demonstrate that it is possible to evaluate an encrypted ResNet20 in less than five minutes on a laptop using approximately 15GB of memory, achieving an accuracy of 91.67% on the CIFAR-10 dataset, which is almost equivalent to the accuracy of the plain model (92.60%).

Private messaging platforms provide strong protection against platform eavesdropping, but malicious users can use privacy as cover for spreading abuse and misinformation. In an attempt to identify the sources of misinformation on private platforms, researchers have proposed mechanisms to trace back the source of a user-reported message (CCS '19,'21). Unfortunately, the threat model considered by initial proposals allowed a single user to compromise the privacy of another user whose legitimate content the reporting user did not like. More recent work has attempted to mitigate this side effect by requiring a threshold number of users to report a message before its origins can be identified (NDSS '22). However, the state of the art scheme requires the introduction of new probabilistic data structures and only achieves a "fuzzy" threshold guarantee. Moreover, false positives, where the source of an unreported message is identified, are possible. This paper introduces a new threshold source tracking technique that allows a private messaging platform, with the cooperation of a third-party moderator, to operate a threshold reporting scheme with exact thresholds and no false positives. Unlike prior work, our techniques require no modification of the message delivery process for a standard source tracking scheme, affecting only the abuse reporting procedure, and do not require tuning of probabilistic data structures.

Imagine being able to deploy a small, battery-powered device nearly anywhere on earth that humans frequent and having it be able to send data to the cloud without needing to provision a network—without buying a physical gateway, setting up WiFi credentials, or acquiring a cellular SIM. Such a capability would address one of the greatest bottlenecks to deploying the long-tail of small, embedded, and power-constrained IoT devices in nearly any setting. Unfortunately, decoupling the device deployment from the network configuration needed to transmit, or backhaul, sensor data to the cloud remains a tricky challenge, but the success of Tile and AirTag offers hope. They have shown that mobile phones can crowd-source worldwide local network coverage to find lost items, yet expanding these systems to enable general-purpose backhaul raises privacy concerns for network participants. In this work, we present Nebula, a privacy-focused architecture for global, intermittent, and low-rate data backhaul to enable nearly any thing to eventually connect to the cloud while (i) preserving the privacy of the mobile network participants from the platform provider by decentralizing data flow through the system, (ii) incentivizing participation through micropayments, and (iii) preventing system abuse.

Electronic voting has long been an area of active and challenging research. Security properties relevant to physical voting in elections with a variety of threat models and priorities are often difficult to reproduce in cryptographic systems and protocols. Existing work in this space often focuses on the privacy of ballot contents, assurances to voters that their votes are tabulated, and verification that election results are correct; however, privacy of voter identity is often offloaded to trust requirements on election organizers or tallying authorities, or implies other kinds of trust related to cryptographic construction instantiation. Here we introduce Aura, an election protocol that reduces trust on tallying authorities and organizers while ensuring voter privacy. Ballots in Aura are dissociated from voter identity cryptographically, use verifiable encryption and threshold decryption to diffuse trust in tallying authorities, require no trusted setup for cryptographic primitives, and use efficient proving systems to reduce computation and communication complexity. These properties make Aura a competitive candidate for use in a variety of applications where trust minimization is desirable or necessary.

Byzantine Agreement (BA) enables $n$ processes to reach consensus on a common valid $L_o$-bit value, even in the presence of up to $t<n$ faulty processes that can deviate arbitrarily from their prescribed protocol. Despite its significance, the optimal communication complexity for key variations of BA has not been determined within the honest majority regime ($n=2t+1$), for both the worst-case scenario and the adaptive scenario, which accounts for the actual number $f \leq t$ of failures. We introduce ADA-DARE (Adaptively Disperse, Agree, Retrieve), a novel universal approach to solve BA efficiently. Let $\kappa$ represent the size of the cryptographic objects required to solve BA when $t>n/3$. Different instantiations of ADA-DARE achieve near-optimal adaptive bit complexity of $O(nL_o + n(f + 1) \kappa)$ for both strong multi-valued validated BA (SMVBA) and interactive consistency (IC). By definition, for IC, $L_o = nL_{in}$, with $L_{in}$ representing the size of an input value. These results achieve optimal $O(n(L_o+f))$ word complexity and significantly improve the previous best results by up to a linear factor, depending on $L_o$ and $f$.

This paper belongs to a sequence of manuscripts that discuss generic and easy-to-apply security metrics for Strong Physical Unclonable Functions (PUFs). These metrics cannot and shall not fully replace in-depth machine learning (ML) studies in the security assessment of Strong PUF candidates. But they can complement the latter, serve in initial complexity analyses, and allow simple iterative design optimization. Moreover, they are computationally more efficient and far easier to standardize than typical ML-studies. This manuscript treats one very natural, but also very impactful metric, and investigates the effects that the alteration of single challenge bits has on the associated PUF-responses. We define several concrete metric scores based on this idea, and demonstrate their predictive power by applying them to various popular Strong PUF design families as test cases. This includes XOR Arbiter PUFs, XOR Bistable Ring PUFs, and Feed-Forward Arbiter PUFs, whose practical security is particularly well known after two decades of intense research. In passing, our manuscript also suggests techniques for representing our metric scores graphically, and for interpreting them in a meaningful manner. Our work demonstrates that if comparable methods had existed earlier, various Strong PUF candidates deemed secure and broken later could have been recognized and winnowed early on.

Many applications relying on cloud storage services typically encrypt their data to ensure data privacy. However, reading or writing the encrypted data to serve client requests reveals the type of client operation to a potentially untrusted cloud. An adversary can exploit this information leak to compromise a user’s privacy by tracking read/write access patterns. Existing approaches such as Oblivious RAM (ORAM) schemes hide the type of client access by always reading and then writing the data sequentially for both reads and writes, rendering one of these rounds redundant with respect to a client request. To mitigate this redundancy, we propose ORTOA- a family of protocols enabling single-round data access on remote storage without revealing the operation type. Specifically, we propose three protocols, two using existing cryptographic primitives of fully homomorphic encryption and trusted execution environments (TEEs), and a new primitive inspired by garbled circuits. Each of these protocols has different trust assumptions, allowing an application to choose the option best suited for its needs. To our knowledge, ORTOA is the first to propose generalized protocols to obfuscate the type of access in a single round, reducing the communication overhead in half. The proposed techniques can pave the way for novel ORAM schemes that hide both the type of access and the access pattern in a single round. Our experimental results show ORTOA achieving throughput gains of 1.7x-3.2x compared to a baseline requiring two rounds for access type concealment, with the baseline incurring latency 1.5-1.9x that of ORTOA for 160B-sized objects.

These Recommendations describe essential elements of the design of a secure physical true random number generator (PTRNG) integrated in an electronic device. Based on these elements, we describe and justify requirements for the design, validation and testing of PTRNGs, which are intended to guarantee the security of generators aimed at cryptographic applications.

Decentralized Anonymous Credential (DAC) systems are increasingly relevant, especially when enhancing revocation mechanisms in the face of complex traceability challenges. This paper introduces IDEA-DAC, a paradigm shift from the conventional revoke-and-reissue methods, promoting direct and Integrity-Driven Editing (IDE) for Accountable DACs, which results in better integrity accountability, traceability, and system simplicity. We further incorporate an Edit-bound Conformity Check that ensures tailored integrity standards during credential amendments using R1CS-based ZK-SNARKs. Delving deeper, we propose ZK-JSON, a unique R1CS circuit design tailored for IDE over generic JSON documents. This design imposes strictly $O(N)$ rank-1 constraints for variable-length JSON documents of up to $N$ bytes in length, encompassing serialization, encryption, and edit-bound conformity checks. Additionally, our circuits only necessitate a one-time compilation, setup, and smart contract deployment for homogeneous JSON documents up to a specified size. While preserving core DAC features such as selective disclosure, anonymity, and predicate provability, IDEA-DAC achieves precise data modification checks without revealing private content, ensuring only authorized edits are permitted. In summary, IDEA-DAC offers an enhanced methodology for large-scale JSON-formatted credential systems, setting a new standard in decentralized identity management efficiency and precision.

The advent of Web3 technologies promises unprecedented levels of user control and autonomy. However, this decentralization shifts the burden of security onto the users, making it crucial to understand their security behaviors and perceptions. To address this, our study introduces a comprehensive framework that identifies four core components of user interaction within the Web3 ecosystem: blockchain infrastructures, Web3-based Decentralized Applications (DApps), online communities, and off-chain cryptocurrency platforms. We delve into the security concerns perceived by users in each of these components and analyze the mitigation strategies they employ, ranging from risk assessment and aversion to diversification and acceptance. We further discuss the landscape of both technical and human-induced security risks in the Web3 ecosystem, identify the unique security differences between Web2 and Web3, and highlight key challenges that render users vulnerable, to provide implications for security design in Web3.

We present a new method for efficient look-up table (LUT) evaluation in homomorphic encryption (HE), based on Ring-LWE-based HE schemes, including both integer-message schemes such as Brakerski-Gentry-Vaikuntanathan (BGV) and Brakerski/Fan-Vercauteren (BFV), and complex-number-message schemes like the Cheon-Kim-Kim-Song (CKKS) scheme. Our approach encodes bit streams into codewords and translates LUTs into low-degree multivariate polynomials, allowing for the simultaneous evaluation of multiple independent LUTs with minimal overhead. To mitigate noise accumulation in the CKKS scheme, we propose a novel noise-reduction technique, accompanied by proof demonstrating its effectiveness in asymptotically decreasing noise levels. We demonstrate our algorithm's effectiveness through a proof-of-concept implementation, showcasing significant efficiency gains, including a 0.029ms per slot evaluation for 8-input, 8-output LUTs and a 280ms amortized decryption time for AES-128 using CKKS on a single GPU. This work not only advances LUT evaluation in HE but also introduces a transciphering method for the CKKS scheme utilizing standard symmetric-key encryption, bridging the gap between discrete bit strings and numerical data.

To improve the throughput of Byzantine Fault Tolerance (BFT) consensus protocols, the Directed Acyclic Graph (DAG) topology has been introduced to parallel data processing, leading to the development of DAG-based BFT consensus. However, existing DAG-based works heavily rely on Reliable Broadcast (RBC) protocols for block broadcasting, which introduces significant latency due to the three communication steps involved in each RBC. For instance, DAGRider, a representative DAG-based protocol, exhibits a best latency of 12 steps, considerably higher than non-DAG protocols like PBFT, which only requires 3 steps. To tackle this issue, we propose LightDAG, which replaces RBC with lightweight broadcasting protocols such as Consistent Broadcast (CBC) and Plain Broadcast (PBC). Since CBC and PBC can be implemented in two and one communication steps, respectively, LightDAG achieves low latency. In our proposal, we present two variants of LightDAG, namely LightDAG1 and LightDAG2, each providing a trade-off between the best latency and the expected worst latency. In LightDAG1, every block is broadcast using CBC, which exhibits a best latency of 5 steps and an expected worst latency of 14 steps. Since CBC cannot guarantee the totality property, we design a block retrieval mechanism in LightDAG1 to assist replicas in retrieving missing blocks. LightDAG2 utilizes a combination of PBC and CBC for block broadcasting, resulting in a best latency of 4 steps and an expected worst latency of $12(t+1)$ steps, where $t$ represents the number of actual Byzantine replicas. Since a Byzantine replica may equivocate through PBC, LightDAG2 prohibits blocks from directly referencing contradictory blocks. To ensure liveness, we propose a mechanism to identify and exclude Byzantine replicas if they engage in equivocation attacks. Extensive experiments have been conducted to evaluate LightDAG, and the results demonstrate its feasibility and efficiency.

In privacy-preserving transaction protocols, confidential asset designs permit transfer of quantities of distinct asset types in a way that obscures their types and values. Spark is a protocol that provides flexible privacy properties relating to addressing, transaction sources and recipients, and value transfer; however, it does not natively support the use of multiple confidential asset types or non-fungible tokens. Here we describe Spats, a new design for confidential assets and serialized tokens compatible with Spark that focuses on efficient and modular implementation. It does so by extending coin value commitments to bind and mask an asset type and identifier, and asserting in zero knowledge that they are maintained throughout transactions. We describe the cryptographic components and changes to the Spark protocol necessary for the design of Spats.

While many anonymous communication (AC) protocols have been proposed to provide anonymity over the internet, scaling to a large number of users while remaining provably secure is challenging. We tackle this challenge by proposing a new scaling technique to improve the scalability/anonymity of AC protocols that distributes the computational load over many nodes without completely disconnecting the paths different messages take through the network. We demonstrate that our scaling technique is useful and practical through a core sample anonymous broadcast protocol, Streams, that offers provable security guarantees and scales for a million messages. The scaling technique ensures that each node in the system does the computation-heavy public key operation only for a tiny fraction of the total messages routed through the Streams network while maximizing the mixing/shuffling in every round. Our experimental results show that Streams can scale well even if the system has a load of one million messages at any point in time, with a latency of 16 seconds while offering provable ``one-in-a-billion'' unlinkability, and can be leveraged for applications such as anonymous microblogging and network-level anonymity for blockchains. We also illustrate by examples that our scaling technique can be useful to other AC protocols to improve their scalability and privacy, and can be interesting to protocol developers.

One approach for scaling blockchains is to create bilateral, offchain channels, known as payment/state channels, that can protect parties against cheating via onchain collateralization. While such channels have been studied extensively, not much attention has been given to programmability, where the parties can agree to dynamically enforce arbitrary conditions over their payments without going onchain. We introduce the notion of a programmable payment channel ($\mathsf{PPC}$) that allows two parties to do exactly this. In particular, our notion of programmability enables the sender of a (unidirectional) payment to dynamically set the terms and conditions for each individual payment using a smart contract. Of course, the verification of the payment conditions (and the payment itself) happens offchain as long as the parties behave honestly. If either party violates any of the terms, then the other party can deploy the smart contract onchain to receive a remedy as agreed upon in the contract. In this paper, we make the following contributions: -- we formalize $\mathsf{PPC}$ as an ideal functionality $\mathcal{F}_{\mathsf{PPC}}$ in the universal composable framework, and build lightweight implementations of applications such as hash timelocked contracts (HTLCs), "reverse HTLCs", and rock-paper-scissors in the $\mathcal{F}_{\mathsf{PPC}}$-hybrid model; -- we show how $\mathcal{F}_{\mathsf{PPC}}$ can be easily modified to capture the state channels functionality $\mathcal{F}_{\mathsf{SC}}$ (described in prior works) where two parties can execute dynamically chosen arbitrary two-party contracts (including those that take deposits from both parties) offchain, i.e., we show how to efficiently realize $\mathcal{F}_{\mathsf{SC}}$ in the $\mathcal{F}_{\mathsf{PPC}}$-hybrid model; -- we show an implementation of $\mathcal{F}_{\mathsf{PPC}}$ on blockchains supporting smart contracts (such as Ethereum), and provide several optimizations to enable concurrent programmable transactions---the gas overhead of an HTLC PPC contract is $<$ 100K, amortized over many offchain payments. We note that our implementations of $\mathcal{F}_{\mathsf{PPC}}$ and $\mathcal{F}_{\mathsf{SC}}$ depend on the CREATE2 opcode which allows one to compute the deployment address of a contract (without having to deploy it).

Battery-operated applications have been ubiquitous all over the world ranging from power-intensive electric cars down to low-power smart terminals and embedded devices. Meanwhile, serious incidents around batteries such as swelling, fire, and explosion have been witnessed, which resulted in horribly huge financial and even life loss. People used to attribute such aftermaths to unintentional design mistakes or insufficient quality inspection of original battery manufacturers. However, this is not fair anymore today given the convoluted battery supply chain and the extended cyber-physical attack surface of battery management systems (BMS). In this paper, we will focus on the authenticity and assurance of prevalent (Li-ion) battery instances. We look into battery authenticity by modeling the contemporary battery supply chain and discussing practical concerns such as rewrapping and recycling in-depth at each stage. As for battery assurance, we consider emerging attack vectors that can compromise the confidentiality, integrity, and availability of the microelectronic BMS. Besides, real-world attack examples are highlighted to reflect the capabilities of advanced adversaries. Moreover, promising countermeasures regarding the detection and avoidance of threats on both battery authenticity and assurance are presented, such that researchers will gain insights into how the problem could be addressed/alleviated. We also provide our perspectives on the vulnerabilities of battery systems and their consequent impacts as well as our point of view on potential countermeasure techniques.

Auctions, a long-standing method of trading goods and services, are a promising use case for decentralized finance. However, due to the inherent transparency property of blockchains, current sealed-bid auction implementations on smart contracts requires a bidder to send at least two transactions to the underlying blockchain: a bidder must first commit their bid in the first transaction during the bidding period and reveal their bid in the second transaction once the revealing period starts. In addition, the smart contract often requires a deposit to incentivize bidders to reveal their bids, rendering unnecessary financial burdens and risks to bidders. We address these drawbacks by enforcing delayed execution in the blockchain execution layer to all transactions. In short, the blockchain only accepts encrypted transactions, and when the blockchain has finalized an encrypted transaction, the consensus group decrypts and executes it. This architecture enables ZeroAuction, a sealed-bid auction smart contract with zero deposit requirement. ZeroAuction relies on the blockchain enhanced with delayed execution to hide and bind the bids within the encrypted transactions and, after a delay period, reveals them automatically by decrypting and executing the transactions. Because a bidder only needs to interact with the blockchain once instead of two times to participate in the auction, ZeroAuction significantly reduces the latency overhead along with eliminating the deposit requirement.

India is the largest democracy by population and has one of the largest deployments of e-voting in the world for national elections. However, the e-voting machines used in India are not end-to-end (E2E) verifiable. The inability to verify the tallying integrity of an election by the public leaves the outcome open to disputes. E2E verifiable e-voting systems are commonly regarded as the most promising solution to address this problem, but they had not been implemented or trialed in India. It was unclear whether such systems would be usable and practical to the Indian people. Previous works such as Helios require a set of tallying authorities (TAs) to perform the decryption and tallying operations, but finding and managing TAs can prove difficult. This paper presents a TA-free E2E verifiable online voting system based on the DRE-ip protocol. In collaboration with the local authority of New Town, Kolkata, India, we conducted an online voting trial as part of the 2022 Durga Puja festival celebration, during which residents of New Town were invited to use mobile phones to vote for their favourite pujas (festival decorations) in an E2E verifiable manner. 543 participants attended the Durga Puja trial and 95 of them provided feedback by filling in an anonymous survey after voting. Based on the voter feedback, participants generally found the system easy to use. This was the first time that an E2E online voting system had been built and tested in India, suggesting its feasibility for non-statutory voting scenarios.

Decentralized Storage Network (DSN) is an emerging technology that challenges traditional cloud-based storage systems by consolidating storage capacities from independent providers and coordinating to provide decentralized storage and retrieval services. However, current DSNs face several challenges associated with data privacy and efficiency of the proof systems. To address these issues, we propose FileDES (Decentralized Encrypted Storage), which incorporates three essential elements: privacy preservation, scalable storage proof, and batch verification. FileDES provides encrypted data storage while maintaining data availability, with a scalable Proof of Encrypted Storage (PoES) algorithm that is resilient to Sybil and Generation attacks. Additionally, we introduce a rollup-based batch verification approach to simultaneously verify multiple files using publicly verifiable succinct proofs. We conducted a comparative evaluation on FileDES, Filecoin, Storj and Sia under various conditions, including a WAN composed of up to 120 geographically dispersed nodes. Our protocol outperforms the others in terms of proof generation/verification efficiency, storage costs, and scalability.

Almost all practical cryptographic protocols are based on computational or ad-hoc assumptions. Assessing the strengths of these assumptions is therefore a key factor in evaluating the risks of the systems using them. As a service to (and by) cryptographic researchers and practitioners, we developed Delphi, an online questionnaire to document researchers' opinions and beliefs about the strengths of the most important assumptions. All responses received will be made accessible on our website, ideally non-anonymous (but depending on the contributor's preference). We also intend to consolidate these responses and publish the results. We believe this effort will be of great value when deciding which cryptographic primitives to keep or start using.

Although having been popular for a long time, Byzantine Fault Tolerance (BFT) consensus under the partially-synchronous network is denounced to be inefficient or even infeasible in recent years, which calls for a more robust asynchronous consensus. On the other hand, almost all the existing asynchronous consensus are too complicated to understand and even suffer from the termination problem. Motivated by the above problems, we propose SimpleFT in this paper, which is a simple asynchronous consensus and is mainly inspired by the simplicity of the Bitcoin protocol. With a re-understanding of the Bitcoin protocol, we disassemble the life cycle of a block into three phases, namely proposal, dissemination, and confirmation. Corresponding to these phases, we devise or introduce the sortition algorithm, reliable broadcast algorithm, and quorum certificate mechanism in SimpleFT, respectively. To make full use of the network resources and improve the system throughput, we further introduce the layered architecture to SimpleFT, which enables multiple blocks to be confirmed at the same height. Comprehensive analysis is made to validate the correctness of SimpleFT and various experiments are conducted to demonstrate its efficient performance.

To reduce latency and communication overhead of asynchronous Byzantine Fault Tolerance (BFT) consensus, an optimistic path is often added, with Ditto and BDT as state-of-the-art representatives. These protocols first attempt to run an optimistic path that is typically adapted from partially-synchronous BFT and promises good performance in good situations. If the optimistic path fails to make progress, these protocols switch to a pessimistic path after a timeout, to guarantee liveness in an asynchronous network. This design crucially relies on an accurate estimation of the network delay Δ to set the timeout parameter correctly. A wrong estimation of Δ can lead to either premature or delayed switching to the pessimistic path, hurting the protocol's efficiency in both cases. To address the above issue, we propose ParBFT, which employs a parallel optimistic path. As long as the leader of the optimistic path is non-faulty, ParBFT ensures low latency without requiring an accurate estimation of the network delay. We propose two variants of ParBFT, namely ParBFT1 and ParBFT2, with a trade-off between latency and communication. ParBFT1 simultaneously launches the two paths, achieves lower latency under a faulty leader, but has a quadratic message complexity even in good situations. ParBFT2 reduces the message complexity in good situations by delaying the pessimistic path, at the cost of a higher latency under a faulty leader. Experimental results demonstrate that ParBFT outperforms Ditto or BDT. In particular, when the network condition is bad, ParBFT can reach consensus through the optimistic path, while Ditto and BDT suffer from path switching and have to make progress using the pessimistic path.

With the growing number of decentralized finance (DeFi) applications, transaction fairness in blockchains has gained much research interest. As a broad concept in distributed systems and blockchains, fairness has been used in different contexts, varying from ones related to the liveness of the system to ones that focus on the received order of transactions. In this work, we revisit the fairness definitions and find that existing fairness definitions are not adapted to blockchains with multiple DApps. We then provide a more generic one called verifiable fairness. Compared with prior definitions, our notion has two unique features: (i) it relaxes the ordering rules to a predicate; (ii) it enables users to independently verify if their transactions comply with the predicate for concrete applications. We also provide a scheme that achieves verifiable fairness, leveraging trusted hardware. Unlike prior works that usually design a dedicated consensus protocol to achieve fairness, our scheme can be integrated with any blockchain system. Our evaluation results on Amazon EC2 using up to 120 instances across different regions show that our construction imposes only minimal overhead on existing blockchain systems.

Homomorphic encryption is a powerful privacy-preserving technology that is notoriously difficult to configure and use, even for experts. The key difficulties include restrictive programming models of homomorphic schemes and choosing suitable parameters for an application. In this tutorial, we outline methodologies to solve these issues and allow for conversion of any application to the encrypted domain using both leveled and fully homomorphic encryption. The first approach, called Walrus, is suitable for arithmetic-intensive applications with limited depth and applications with high throughput requirements. Walrus provides an intuitive programming interface and handles parameterization automatically by analyzing the application and gathering statistics such as homomorphic noise growth to derive a parameter set tuned specifically for the application. We provide an in-depth example of this approach in the form of a neural network inference as well as guidelines for using Walrus effectively. Conversely, the second approach (HELM) takes existing HDL designs and converts them to the encrypted domain for secure outsourcing on powerful cloud servers. Unlike Walrus, HELM supports FHE backends and is well-suited for complex applications. At a high level, HELM consumes netlists and is capable of performing logic gate operations homomorphically on encryptions of individual bits. HELM incorporates both CPU and GPU acceleration by taking advantage of the inherent parallelism provided by Boolean circuits. As a case study, we walk through the process of taking an off-the-shelf HDL design in the form of AES-128 decryption and running it in the encrypted domain with HELM.

This paper describes Djed, an algorithmic stablecoin protocol that behaves like an autonomous bank that buys and sells stablecoins for a price in a range that is pegged to a target price. It is crypto-backed in the sense that the bank keeps a volatile cryptocurrency in its reserve. The reserve is used to buy stablecoins from users that want to sell them. And revenue from sales of stablecoins to users are stored in the reserve. Besides stablecoins, the bank also trades reservecoins in order to capitalize itself and maintain a reserve ratio significantly greater than one. To the best of our knowledge, this is the first stablecoin protocol where stability claims are precisely and mathematically stated and proven. Furthermore, the claims and their proofs are formally verified using two different techniques: bounded model checking, to exhaustively search for counter-examples to the claims; and interactive theorem proving, to build rigorous formal proofs using a proof assistant with automated theorem proving features.

Recommender systems are effective mechanisms for recommendations about what to watch, read, or taste based on user ratings about experienced products or services. To achieve higher quality recommendations, e-commerce parties may prefer to collaborate over partitioned data. Due to privacy issues, they might hesitate to work in pairs and some solutions motivate them to collaborate. This study examines how to estimate trust-based predictions on arbitrarily partitioned data in which two parties have ratings for similar sets of customers and items. A privacy- preserving scheme is proposed, and it is justified that it efficiently offers trust-based predictions on partitioned data while preserving privacy.

The use of zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARK) and similar types of proofs has become increasingly popular as a solution for improving scalability, privacy, and interoperability of blockchain systems. However, even with the most advanced proving systems, verifying a single SNARK proof can require a significant amount of computational resources making it expensive to be performed on-chain. This becomes a noticeable bottleneck in scaling SNARK-based applications. Further efficiency improvement to avoid this bottleneck lies in utilizing distributed recursive proof composition to aggregate multiple existing proofs into one that verifies all underlying proofs. Building upon this concept, we present a new protocol for decentralized recursive proof aggregation allowing one unique proof to aggregate many input proofs to be efficiently verified on-chain, increasing the throughput and cost efficiency of SNARK-based blockchains. The protocol is designed for decentralized environments where independent actors (provers) can join and contribute to the proof generation process. We also present an incentive scheme for such actors. The protocol is abstract enough to be used with a variety of proving systems that support recursive aggregation.

Federated Learning (FL) is a data-minimization approach enabling collaborative model training across diverse clients with local data, avoiding direct data exchange. However, state-of-the-art FL solutions to identify fraudulent financial transactions exhibit a subset of the following limitations. They (1) lack a formal security definition and proof, (2) assume prior freezing of suspicious customers’ accounts by financial institutions (limiting the solutions’ adoption), (3) scale poorly, involving either $O(n^2)$ computationally expensive modular exponentiation (where $n$ is the total number of financial institutions) or highly inefficient fully homomorphic encryption, (4) assume the parties have already completed the identity alignment phase, hence excluding it from the implementation, performance evaluation, and security analysis, and (5) struggle to resist clients’ dropouts. This work introduces Starlit, a novel scalable privacy-preserving FL mechanism that overcomes these limitations. It has various applications, such as enhancing financial fraud detection, mitigating terrorism, and enhancing digital health. We implemented Starlit and conducted a thorough performance analysis using synthetic data from a key player in global financial transactions. The evaluation indicates Starlit’s scalability, efficiency, and accuracy.

Federated Learning (FL) accomplishes collaborative model training without the need to share local training data. However, existing FL aggregation approaches suffer from inefficiency, privacy vulnerabilities, and neglect of poisoning attacks, severely impacting the overall performance and reliability of model training. In order to address these challenges, we propose SuperFL, an efficient two-server aggregation scheme that is both privacy preserving and secure against poisoning attacks. The two semi-honest servers $\mathcal{S}_0$ and $\mathcal{S}_1$ collaborate with each other, with a shuffle server $\mathcal{S}_0$ in charge of privacy-preserving random clustering, while an analysis server $\mathcal{S}_1$ responsible for robustness detection, identifying and filtering malicious model updates. Our scheme employs a novel combination of homomorphic encryption and proxy re-encryption to realize secure server-to-server collaboration. We also utilize a novel sparse matrix projection compression technique to enhance communication efficiency and significantly reduce communication overhead. To resist poisoning attacks, we introduce a dual-filter algorithm based on trusted root, combine dimensionality reduction and norm calculation to identify malicious model updates. Extensive experiments validate the efficiency and robustness of our scheme. SuperFL achieves impressive compression ratios, ranging from $5\text{-}40$x, under different models while maintaining comparable model accuracy as the baseline. Notably, our solution demonstrates a maximal model accuracy decrease of no more than $2\%$ and $6\%$ on the MNIST and CIFAR-10 datasets respectively, under specific compression ratios and the presence of malicious clients.

Decentralized applications (DApps), which are innovative blockchain-powered software systems designed to serve as the fundamental building blocks for the next generation of Internet services, have witnessed exponential growth in recent years. This paper thoroughly compares and analyzes two blockchain-based decentralized storage networks (DSNs), which are crucial foundations for DApp and blockchain ecosystems. The study examines their respective mechanisms for data persistence, strategies for enforcing data retention, and token economics. In addition to delving into technical details, the suitability of each storage solution for decentralized application development is assessed, taking into consideration network performance, storage costs, and existing use cases. By evaluating these factors, the paper aims to provide insights into the effectiveness of these technologies in supporting the desirable properties of truly decentralized blockchain applications. In conclusion, the findings of this research are discussed and synthesized, offering valuable perspectives on the capabilities of these technologies. It sheds light on their potential to facilitate the development of DApps and provides an understanding of the ongoing trends in blockchain development.

Competitions are widely viewed as the safest way to select cryptographic algorithms. This paper surveys procedures that have been used in cryptographic competitions, and analyzes the extent to which those procedures reduce security risks.

Zero-Knowledge Proofs (ZKPs), a cryptographic tool known for decades, have gained significant attention in recent years due to advancements that have made them practically applicable in real-world scenarios. ZKPs can provide unique attributes, such as succinctness, non-interactivity, and the ability to prove knowledge without revealing the information itself, making them an attractive solution for a range of applications. This paper aims to critically analyze the applicability of ZKPs in various scenarios. We categorize ZKPs into distinct types: SNARKs (Succinct Non-Interactive Arguments of Knowledge), Commit-then-Prove ZKPs, MPC-in-the-Head, and Sigma Protocols, each offering different trade-offs and benefits. We introduce a flowchart methodology to assist in determining the most suitable ZKP system, given a set of technical application requirements. Next, we conduct an in-depth investigation of three major use cases: Outsourcing Computation, Digital Self-Sovereign Identity, and ZKPs in networking. Additionally, we provide a high-level overview of other applications of ZKPs, exploring their broader implications and opportunities. This paper aims to demystify the decision-making process involved in choosing the right ZKP system, providing clarity on when and how these cryptographic tools can be effectively utilized in various domains — and when they are better to be avoided.

The Lightning Network (LN) is a second layer solution built on top of Bitcoin, aimed to solve Bitcoin's long transaction waiting times and high transaction fees. Empirical and theoretical studies show that the LN is tending towards the hub and spoke network topology. In this topology most of the nodes, the spokes, open a single channel to one of the few well-connected nodes, the hubs. This topology is known to be prone to failures, attacks, and privacy issues. In this work we introduce the Maypoles protocol in which most nodes open two channels instead of one. We show that this protocol benefits the network significantly by enhancing its stability, privacy, and resilience to attacks. We also examine the economic incentives of nodes to take part in Maypoles.

Applying the pothole method on the factors of numbers of the form $2^n-1$, we prove that if $2^n-1$ has carries of degree at most $$\kappa(2^n-1)=\frac{1}{2(1+c)}\lfloor \frac{\log n}{\log 2}\rfloor-1$$ for $c>0$ fixed, then the inequality $$\iota(2^n-1)\leq n-1+(1+\frac{1}{1+c})\lfloor\frac{\log n}{\log 2}\rfloor$$ holds for all $n\in \mathbb{N}$ with $n\geq 4$, where $\iota(\cdot)$ denotes the length of the shortest addition chain producing $\cdot$. In general, we show that all numbers of the form $2^n-1$ with carries of degree $$\kappa(2^n-1):=(\frac{1}{1+f(n)})\lfloor \frac{\log n}{\log 2}\rfloor-1$$ with $f(n)=o(\log n)$ and $f(n)\longrightarrow \infty$ as $n\longrightarrow \infty$ for $n\geq 4$ then the inequality $$\iota(2^n-1)\leq n-1+(1+\frac{2}{1+f(n)})\lfloor\frac{\log n}{\log 2}\rfloor$$ holds.

Decentralized anonymous payment schemes may be exploited for illicit activities, such as money laundering, bribery and blackmail. To address this issue, several regulatory friendly decentralized anonymous payment schemes have been proposed. However, most of these solutions lack restrictions on the regulator’s authority, which could potentially result in power abuse and privacy breaches. In this paper, we present a decentralized anonymous payment scheme with collaborative regulation (DAPCR). Unlike existing solutions, DAPCR reduces the risk of power abuse by distributing regulatory authority to two entities: Filter and Supervisor, neither of which can decode transactions to access transaction privacy without the assistance of the other one. Our scheme enjoys three major advantages over others: ① Universality, achieved by using zk-SNARK to extend privacy-preserving transactions for regulation. ② Collab orative regulation, attained by adding the ring signature with controllable linkability to the transaction. ③ Efficient aggregation of payment amounts, achieved through amount tags. As a key technology for realizing collaborative regulation in DAPCR, the ring signature with controllable linkability (CLRS) is proposed, where a user needs to specify a linker and an opener to generate a signature. The linker can extract pseudonyms from signatures and link signatures submitted by the same signer based on pseudonyms, without leaking the signer’s identity. The opener can recover the signer’s identity from a given pseudonym. The experimental results reflect the efficiency of DAPCR. The time overhead for transaction generation is 1231.2 ms, representing an increase of less than 50 % compared to ZETH. Additionally, the time overhead for transaction verification is only 1.2 ms.

Success Rate (SR) is one of the most popular security metrics measuring the efficiency of side-channel attacks. Theoretical expression reveals the functional dependency on critical parameters such as number of measurements and Signal-to-Noise Ratio (SNR), helping evaluators understand the threat of an attack as well as how one can mitigate it with proper countermeasures. However so far, existing works have exposed fundamental problems such as: (i) the evaluations are restricted to a very limited number of distinguishers and the methods in the literature seem specialized (i.e., hard to be extended). (ii) the evaluations assume an a-priori perfect leakage model which lacks practical relevance and ignores the fact that inaccurate profiling may lead to information loss and distorted SR. In this paper, we tackle above problems by providing an evaluation framework where different univariate DPA distinguishers are intuitively unified as linear maximum likelihood attack seeking for the closest `distance' between vectors in Euclidean space. We argue that this is an intrinsic property of the DPA mechanism and is independent of the leakage model. Then, we abstract the concept of SR and derive the theoretical expression in a geometric way. Finally, the theory allows a further study on leakage model where we formalize criterion explaining the impact of model errors as well as guaranteeing robust performance. We transfer the model effects to a degraded SNR parameter. Experimental results are inline with the theory, confirming that our theoretical expression coincides with the empirical ones.

Artificial intelligence (AI) is a modern technology that allows plenty of advantages in daily life, such as predicting weather, finding directions, classifying images and videos, even automatically generating code, text, and videos. Other essential technologies such as blockchain and cybersecurity also benefit from AI. As a core component used in blockchain and cybersecurity, cryptography can benefit from AI in order to enhance the confidentiality and integrity of cyberspace. In this paper, we review the algorithms underlying four prominent cryptographic cryptosystems, namely the Advanced Encryption Standard, the Rivest--Shamir--Adleman, Learning With Errors, and the Ascon family of cryptographic algorithms for authenticated encryption. Where possible, we pinpoint areas where AI can be used to help improve their security.

Recent years have witnessed significant advances in programming technology for multi-party computation (MPC), bringing MPC closer to practice and wider applicability. Typical MPC programming frameworks focus on either front-end language design (e.g., Wysteria, Viaduct, SPDZ), or back-end protocol implementation (e.g., ABY, MOTION, SPDZ). We propose a methodology for an MPC compilation toolchain, which by mimicking the compilation methodology of classical compilers enables middle-end (i.e., machine-independent) optimizations, yielding significant improvements. We advance an intermediate language, which we call MPC-IR that can be viewed as the analogue of (enriched) Static Single Assignment (SSA) form. MPC-IR enables backend-independent optimizations in a close analogy to machine-independent optimizations in classical compilers. To demonstrate our approach, we focus on a specific backend-independent optimization, SIMD-vectorization: We devise a novel classical-compiler-inspired automatic SIMD vectorization on MPC-IR. To demonstrate backend independence and quality of our optimization, we evaluate our approach with two mainstream backend frameworks that support multiple types of MPC protocols, namely MOTION and MP-SPDZ, and show significant improvements across the board.

Private computation of nonlinear functions, such as Rectified Linear Units (ReLUs) and max-pooling operations, in deep neural networks (DNNs) poses significant challenges in terms of storage, bandwidth, and time consumption. To address these challenges, there has been a growing interest in utilizing privacy-preserving techniques that leverage polynomial activation functions and kernelized convolutions as alternatives to traditional ReLUs. However, these alternative approaches often suffer from a trade-off between achieving faster private inference (PI) and sacrificing model accuracy. In particular, when applied to much deeper networks, these methods encounter training instabilities, leading to issues like exploding gradients (resulting in NaNs) or suboptimal approximations. In this study, we focus on PolyKervNets, a technique known for offering improved dynamic approximations in smaller networks but still facing instabilities in larger and more complex networks. Our primary objective is to empirically explore optimization-based training recipes to enhance the performance of PolyKervNets in larger networks. By doing so, we aim to potentially eliminate the need for traditional nonlinear activation functions, thereby advancing the state-of-the-art in privacy-preserving deep neural network architectures.

We propose a new compiler framework that automates code generation over multiple fully homomorphic encryption (FHE) schemes. While it was recently shown that algorithms combining multiple FHE schemes (e.g., CKKS and TFHE) achieve high execution efficiency and task utility at the same time, developing fast cross-scheme FHE algorithms for real-world applications generally require heavy hand-tuned optimizations by cryptographic experts, resulting in either high usability costs or low computational efficiency. To solve the usability and efficiency dilemma, we design and implement HEIR, a compiler framework based on multi-level intermediate representation (IR). To achieve cross-scheme compilation of efficient FHE circuits, we develop a two-stage code-lowering structure based on our custom IR dialects. First, the plaintext program along with the associated data types are converted into FHE-friendly dialects in the transformation stage. Then, in the optimization stage, we apply FHE-specific optimizations to lower the transformed dialect into our bottom-level FHE library operators. In the experiment, we implement the entire software stack for HEIR, and demonstrate that complex end-to-end programs, such as homomorphic K-Means clustering and homomorphic data aggregation in databases, can easily be compiled to run $72$--$179\times$ faster than the program generated by the state-of-the-art FHE compilers.

Use of game theory and mechanism design in cloud security is a well-studied topic. When applicable, it has the advantages of being efficient and simple compared to cryptography alone. Most analyses consider two-party settings, or multi-party settings where coalitions are not allowed. However, many cloud security problems that we face are in the multi-party setting and the involved parties can almost freely collaborate with each other. To formalize the study of disincentivizing coalitions from deviating strategies, a well-known definition named k-resiliency has been proposed by Abraham et al. (ACM PODC '06). Since its proposal, k-resiliency and related definitions are used extensively for mechanism design. However, in this work we observe the shortcoming of k-resiliency. That is, although this definition is secure, it is too strict to use for many cases and rule out secure mechanisms as insecure. To overcome this issue, we propose a new definition named l-repellence against the presence of a single coalition to replace k-resiliency. Our definition incorporates transferable utility in game theory as it is realistic in many distributed and multi-party computing settings. We also propose m-stability definition against the presence of multiple coalitions, which is inspired by threshold security in cryptography. We then show the advantages of our novel definitions on three mechanisms, none of which were previously analyzed against coalitions: incentivized cloud computation, forwarding data packages in ad hoc networks, and connectivity in ad hoc networks. Regarding the former, our concepts improve the proposal by Küpçü (IEEE TDSC '17), by ensuring a coalition-proof mechanism.

Considerable work explores blockchain privacy notions. Yet, it usually employs entirely different models and notations, complicating potential comparisons. In this work, we use the Transaction Directed Acyclic Graph (TDAG) and extend it to capture blockchain privacy notions (PDAG). We give consistent definitions for untraceability and unlinkability. Moreover, we specify conditions on a blockchain system to achieve each aforementioned privacy notion. Thus, we can compare the two most prominent privacy-preserving blockchains -- Monero and Zcash, in terms of privacy guarantees. Finally, we unify linking heuristics from the literature with our graph notation and review a good portion of research on blockchain privacy.

Collateral is an item of value serving as security for the repayment of a loan. In blockchain-based loans, cryptocurrencies serve as the collateral. The high volatility of cryptocurrencies implies a serious barrier of entry with a common practice that collateral values equal multiple times the value of the loan. As assets serving as collateral are locked, this requirement prevents many candidates from obtaining loans. In this paper, we aim to make loans more accessible by offering loans with lower collateral, while keeping the risk for lenders bound. We propose a credit score based on data recovered from the blockchain to predict how likely a potential borrower is to repay a loan. Our protocol does not risk the initial amount granted by liquidity providers, but only risks part of the interest yield gained from the borrower by the protocol in the past.

Blockchain interoperability refers to the ability of blockchains to share information with each other. Decentralized Exchanges (DEXs) are peer-to-peer marketplaces where traders can exchange cryptocurrencies. Several studies have focused on arbitrage analysis within a single blockchain, typically in Ethereum. Recently, we have seen a growing interest in cross-chain technologies to create a more interconnected blockchain network. We present a framework to study cross-chain arbitrage in DEXs. We use this framework to analyze cross-chain arbitrages between two popular DEXs, PancakeSwap and QuickSwap, within a time frame of a month. While PancakeSwap is implemented on a blockchain named BNB Chain, QuickSwap is implemented on a different blockchain named Polygon. The approach of this work is to study the cross-chain arbitrage through an empirical study. We refer to the number of arbitrages, their revenue as well as to their duration. This work lays the basis for understanding cross-chain arbitrage and its potential impact on the blockchain technology.

The recent advancements in deep learning have brought about significant changes in various aspects of people's lives. Meanwhile, these rapid developments have raised concerns about the legitimacy of the training process of deep neural networks. To protect the intellectual properties of AI developers, directly examining the training process by accessing the model parameters and training data is often prohibited for verifiers. In response to this challenge, we present zero-knowledge deep learning (zkDL), an efficient zero-knowledge proof for deep learning training. To address the long-standing challenge of verifiable computations of non-linearities in deep learning training, we introduce zkReLU, a specialized proof for the ReLU activation and its backpropagation. zkReLU turns the disadvantage of non-arithmetic relations into an advantage, leading to the creation of FAC4DNN, our specialized arithmetic circuit design for modelling neural networks. This design aggregates the proofs over different layers and training steps, without being constrained by their sequential order in the training process. With our new CUDA implementation that achieves full compatibility with the tensor structures and the aggregated proof design, zkDL enables the generation of complete and sound proofs in less than a second per batch update for an 8-layer neural network with 10M parameters and a batch size of 64, while provably ensuring the privacy of data and model parameters. To our best knowledge, we are not aware of any existing work on zero-knowledge proof of deep learning training that is scalable to million-size networks.

Explainable AI (XAI) refers to the development of AI systems and machine learning models in a way that humans can understand, interpret and trust the predictions, decisions and outputs of these models. A common approach to explainability is feature importance, that is, determining which input features of the model have the most significant impact on the model prediction. Two major techniques for computing feature importance are LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). While very generic, these methods are computationally expensive even in plaintext. Applying them in the privacy-preserving setting when part or all of the input data is private is therefore a major computational challenge. In this paper, we present $\texttt{XorSHAP}$ - the first practical privacy-preserving algorithm for computing Shapley values for decision tree ensemble models in the semi-honest Secure Multiparty Computation (SMPC) setting with full threshold. Our algorithm has complexity $O(T \widetilde{M} D 2^D)$, where $T$ is the number of decision trees in the ensemble, $D$ is the depth of the decision trees and $\widetilde{M}$ is the maximum of the number of features $M$ and $2^D$ (the number of leaf nodes of a tree), and scales to real-world datasets. Our implementation is based on Inpher's $\texttt{Manticore}$ framework and simultaneously computes (in the SMPC setting) the Shapley values for 100 samples for an ensemble of $T = 60$ trees of depth $D = 4$ and $M = 100$ features in just 7.5 minutes, meaning that the Shapley values for a single prediction are computed in just 4.5 seconds for the same decision tree ensemble model. Additionally, it is parallelization-friendly, thus, enabling future work on massive hardware acceleration with GPUs.

Syndrome-based early epidemic warning plays a vital role in preventing and controlling unknown epidemic outbreaks. It monitors the frequency of each syndrome, issues a warning if some frequency is aberrant, identifies potential epidemic outbreaks, and alerts governments as early as possible. Existing systems adopt a cloud-assisted paradigm to achieve cross-facility statistics on the syndrome frequencies. However, in these systems, all symptom data would be directly leaked to the cloud, which causes critical security and privacy issues. In this paper, we first analyze syndrome-based early epidemic warning systems and formalize two security notions, i.e., symptom confidentiality and frequency confidentiality, according to the inherent security requirements. We propose EpiOracle, a cross-facility early warning scheme for unknown epidemics. EpiOracle ensures that the contents and frequencies of syndromes will not be leaked to any unrelated parties; moreover, our construction uses only a symmetric-key encryption algorithm and cryptographic hash functions (e.g., [CBC]AES and SHA-3), making it highly efficient. We formally prove the security of EpiOracle in the random oracle model. We also implement an EpiOracle prototype and evaluate its performance using a set of real-world symptom lists. The evaluation results demonstrate its practical efficiency.

Vehicle Ad Hoc Networks (VANETs) play a pivotal role in intelligent transportation systems, offering dynamic communication between vehicles, Road Side Units (RSUs), and the internet. Given the open-access nature of VANETs and the associated threats, such as impersonation and privacy violations, ensuring the security of these communications is of utmost importance. This paper presents the Identity-based Cluster Authentication and Key Exchange (ID-CAKE) scheme, a new approach to address security challenges in VANETs. The ID-CAKE scheme integrates the Cluster Consensus Identity-based Identification (CCIBI) with Zero-Knowledge (ZK) proofs and the Identity-based Multireceiver Key Exchange Mechanism (ID-mKEM) signature scheme. This integration provides robust authorization via CCIBI, while ID-mKEM signatures ensure message integrity, and guarantee both non-repudiation and unforgeability through mKEM for message broadcasting. The scheme employs a novel three-party ZK proof for batch verification using mKEM, which significantly reduces computational burdens. Our scheme also ensures anonymity and unlinkability by introducing pseudo-identities to all users in the cluster. The rigorous security proofs provided confirm the resilience of the ID-CAKE scheme against potential attacks, adhering to the different scenarios, against the hardness of the elliptic curve computational Diffie-Hellman under the random oracle model. The ID-CAKE scheme establishes a robust security framework for VANETs, and its introduction highlights potential pathways for future exploration in the realm of VANET security.

Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity. Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV), which undermines both the consensus security and efficiency of blockchain systems. The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more sophisticated MEV extraction. In this paper, we conduct the first systematic study on DeFi MEV activities in Flashbots bundle by developing ActLifter, a novel automated tool for accurately identifying DeFi actions in transactions of each bundle, and ActCluster, a new approach that leverages iterative clustering to facilitate us to discover known/unknown DeFi MEV activities. Extensive experimental results show that ActLifter can achieve nearly 100% precision and recall in DeFi action identification, significantly outperforming state-of-the-art techniques. Moreover, with the help of ActCluster, we obtain many new observations and discover 17 new kinds of DeFi MEV activities, which occur in 53.12% of bundles but have not been reported in existing studies.

In this work, we study and formalize security notions for algorithm substitution attacks (ASAs) on em cryptographic puzzles. Puzzles are difficult problems that require an investment of computation, memory, or some other related resource. They are heavily used as a building block for the consensus networks used by cryptocurrencies. These include primitives such as proof-of-work, proof-of-space, and verifiable delay functions (VDFs). Due to economies of scale, these networks increasingly rely on a small number of companies to construct opaque hardware or software (e.g., GPU or FPGA images): this dependency raises concerns about cryptographic subversion. Unlike the algorithms considered by previous ASAs, cryptographic puzzles do not rely on secret keys and thus enable a very different set of attacks. We first explore the threat model for these systems and then propose concrete attacks that (1) selectively reduce a victim's solving capability ( e.g., hashrate) and (2) exfiltrate puzzle solutions to an attacker. We then propose defenses, several of which can be applied to existing cryptocurrency hardware with minimal changes. We also find that mining devices for many major proof-of-work cryptocurrencies already demonstrate errors exactly how a potentially subverted device would. Given that these attacks are relevant to all proof of work cryptocurrencies that have a combined market capitalization of around a few hundred billion dollars (2022), we recommend that all vulnerable mining protocols consider making the suggested adaptations today.

In recent years, quantum computers and Shor’s quantum algorithm have been able to effectively solve NP (Non-deterministic Polynomial-time) problems such as prime factorization and discrete logarithm problems, posing a threat to current mainstream asymmetric cryptography, including RSA and Elliptic Curve Cryptography (ECC). As a result, the National Institute of Standards and Technology (NIST) in the United States call for Post-Quantum Cryptography (PQC) methods that include lattice-based cryptography methods, code-based cryptography methods, multivariate cryptography methods, and hash-based cryptography methods for resisting quantum computing attacks. Therefore, this study proposes a PQC neural network (PQC-NN) that maps a code-based PQC method to a neural network structure and enhances the security of ciphertexts with non-linear activation functions, random perturbation of ciphertexts, and uniform distribution of ciphertexts. The main innovations of this study include: (1) constructing a neural network structure that complies with code-based PQC, where the weight sets between the input layer and the ciphertext layer can be used as a public key for encryption, and the weight sets between the ciphertext layer and the output layer can be used as a private key for decryption; (2) adding random perturbations to the ciphertext layer, which can be removed during the decryption phase to restore the original plaintext; (3) constraining the output values of the ciphertext layer to follow a uniform distribution with a significant similarity by adding the cumulative distribution function (CDF) values of the chi-square distribution to the loss function, ensuring that the neural network produces sufficiently uniform distribution for the output values of the ciphertext layer. In practical experiments, this study uses cellular network signals as a case study to demonstrate that encryption and decryption can be performed by the proposed PQC neural network with the uniform distribution of ciphertexts. In the future, the proposed PQC neural network could be applied to various applications.

We study the problem of committee selection in the context of proof-of-stake consensus mechanisms or distributed ledgers. These settings determine a family of participating parties---each of which has been assigned a non-negative "stake"---and are subject to an adversary that may corrupt a subset of the parties. The challenge is to select a committee of participants that accurately reflects the proportion of corrupt and honest parties, as measured by stake, in the full population. The trade-off between committee size and the probability of selecting a committee that over-represents the corrupt parties is a fundamental factor in both security and efficiency of proof-of-stake consensus, as well as committee-run layer-two protocols. We propose and analyze several new committee selection schemes that improve upon existing techniques by adopting low-variance assignment of certain committee members that hold significant stake. These schemes provide notable improvements to the size--security trade-off arising from the stake distributions of many deployed ledgers.

The Algorand consensus protocol is interesting both in theory and in practice. On the theoretical side, to achieve adaptive security, it introduces the novel idea of player replaceability, where each step of the protocol is executed by a different randomly selected committee whose members remain secret until they send their first and only message. The protocol provides consistency under arbitrary network conditions and liveness under intermittent network partitions. On the practical side, the protocol is used to secure the Algorand cryptocurrency, whose total value is approximately $850M at the time of writing. The Algorand protocol in use differs substantially from the protocols described in the published literature on Algorand. Despite its significance, it lacks a formal analysis. In this work, we describe and analyze the Algorand consensus protocol as deployed today in Algorand’s ecosystem. We show that the overall protocol framework is sound by characterizing network conditions and parameter settings under which the protocol can be proven secure.

Payment Channel Hub (PCH) is a promising solution to the scalability issue of first-generation blockchains or cryptocurrencies such as Bitcoin. It supports off-chain payments between a sender and a receiver through an intermediary (called the tumbler). Relationship anonymity and value privacy are desirable features of privacy-preserving PCHs, which prevent the tumbler from identifying the sender and receiver pairs as well as the payment amounts. To our knowledge, all existing Bitcoin-compatible PCH constructions that guarantee relationship anonymity allow only a (predefined) fixed payment amount. Thus, to achieve payments with different amounts, they would require either multiple PCH systems or running one PCH system multiple times. Neither of these solutions would be deemed practical. In this paper, we propose the first Bitcoin-compatible PCH that achieves relationship anonymity and supports variable amounts for payment. To achieve this, we have several layers of technical constructions, each of which could be of independent interest to the community. First, we propose $\textit{BlindChannel}$, a novel bi-directional payment channel protocol for privacy-preserving payments, where {one of the channel parties} is unable to see the channel balances. Then, we further propose $\textit{BlindHub}$, a three-party (sender, tumbler, receiver) protocol for private conditional payments, where the tumbler pays to the receiver only if the sender pays to the tumbler. The appealing additional feature of BlindHub is that the tumbler cannot link the sender and the receiver while supporting a variable payment amount. To construct BlindHub, we also introduce two new cryptographic primitives as building blocks, namely $\textit{Blind Adaptor Signature}$(BAS), and $\textit{Flexible Blind Conditional Signature}$. BAS is an adaptor signature protocol built on top of a blind signature scheme. Flexible Blind Conditional Signature is a new cryptographic notion enabling us to provide an atomic and privacy-preserving PCH. Lastly, we instantiate both BlindChannel and BlindHub protocols and present implementation results to show their practicality.

Zero-knowledge middleboxes (ZKMBs) are a recent paradigm in which clients get privacy while middleboxes enforce policy: clients prove in zero knowledge that the plaintext underlying their encrypted traffic complies with network policies, such as DNS filtering. However, prior work had impractically poor performance and was limited in functionality. This work presents Zombie, the first system built using the ZKMB paradigm. Zombie introduces techniques that push ZKMBs to the verge of practicality: preprocessing (to move the bulk of proof generation to idle times between requests), asynchrony (to remove proving and verifying costs from the critical path), and batching (to amortize some of the verification work). Zombie’s choices, together with these techniques, reduce client and middlebox overhead by $\approx$ 3.5$\times$ lowering the critical path overhead for a DNS filtering application on commodity hardware to less than 300ms or, in the asynchronous configuration, to 0. As an additional contribution that is likely of independent interest, Zombie introduces a portfolio of techniques to efficiently encode regular expressions in probabilistic (and zero knowledge) proofs; these techniques offer significant asymptotic and constant factor improvements in performance over a standard baseline. Zombie builds on this portfolio to support policies based on regular expressions, such as data loss prevention.

The privacy concern in smart contract applications continues to grow, leading to the proposal of various schemes aimed at developing comprehensive and universally applicable privacy-preserving smart contract (PPSC) schemes. However, the existing research in this area is fragmented and lacks a comprehensive system overview. This paper aims to bridge the existing research gap on PPSC schemes by systematizing previous studies in this field. The primary focus is on two categories: PPSC schemes based on cryptographic tools like zero-knowledge proofs, as well as schemes based on trusted execution environments. In doing so, we aim to provide a condensed summary of the different approaches taken in constructing PPSC schemes. Additionally, we also offer a comparative analysis of these approaches, highlighting the similarities and differences between them. Furthermore, we shed light on the challenges that developers face when designing and implementing PPSC schemes. Finally, we delve into potential future directions for improving and advancing these schemes, discussing possible avenues for further research and development.

Confidential Computing is the protection of data in use from access or modification by any unauthorized agent, including privileged software. For example, in Intel SGX (Client and Scalable versions) and TDX, AMD SEV, Arm CCA, and IBM Ultravisor this protection is implemented via access control policies. Some of these architectures also include memory protection schemes relying on cryptography, to protect against physical attacks. We review and classify such schemes, from academia and industry, according to protection levels corresponding of adversaries with varying capabilities, budget, and strategy. The building blocks of all memory protection schemes are encryption and integrity primitives and modes of operation, as well as anti-replay structures. We review these building blocks, consider their possible combinations, and evaluate the performance impact of the resulting schemes. We present a framework for performance evaluation in a simulated system. To understand the best and worst case overhead, systems with varying load levels are considered. We propose new solutions to further reduce the performance and memory overheads of such technologies. Advanced counter compression techniques make it viable to store counters used for replay protection in a physically protected memory. By additionally repurposing some ECC bits to store integrity tags, we can provide the highest levels of confidentiality, integrity, and replay protection at a hitherto unattained performance penalty, namely 3.32%, even under extreme load and at costs that make them reasonable in data centers. Combinations of technologies that are suitable for client devices are also discussed.

Domain Generation Algorithms (DGAs) are used by malware to generate pseudorandom domain names to establish communication between infected bots and Command and Control servers. While DGAs can be detected by machine learning (ML) models with great accuracy, offering DGA detection as a service raises privacy concerns when requiring network administrators to disclose their DNS traffic to the service provider. We propose the first end-to-end framework for privacy-preserving classification as a service of domain names into DGA (malicious) or non-DGA (benign) domains. We achieve this through a combination of two privacy-enhancing technologies (PETs), namely secure multi-party computation (MPC) and differential privacy (DP). Through MPC, our framework enables an enterprise network administrator to outsource the problem of classifying a DNS domain as DGA or non-DGA to an external organization without revealing any information about the domain name. Moreover, the service provider's ML model used for DGA detection is never revealed to the network administrator. Furthermore, by using DP, we also ensure that the classification result cannot be used to learn information about individual entries of the training data. Finally, we leverage the benefits of quantization of deep learning models in the context of MPC to achieve efficient, secure DGA detection. We demonstrate that we achieve a significant speed-up resulting in a 15% reduction in detection runtime without reducing accuracy.

The global lockdown imposed during the Covid-19 pandemic has resulted in significant social and economic challenges. In an effort to reopen economies and simultaneously control the spread of the disease, the implementation of contact tracing and digital vaccine passport technologies has been introduced. While contact tracing methods have been extensively studied and scrutinized for security concerns through numerous publications, vaccine passports have not received the same level of attention in terms of defining the problems they address, establishing security requirements, or developing efficient systems. Many of the existing methods employed currently suffer from privacy issues. This work introduces PPass, an advanced digital vaccine passport system that prioritizes user privacy. We begin by outlining the essential security requirements for an ideal vaccine passport system. To address these requirements, we present two efficient constructions that enable PPass to function effectively across various environments while upholding user privacy. By estimating its performance, we demonstrate the practical feasibility of PPass. Our findings suggest that PPass can efficiently verify a passenger’s vaccine passport in just 7 milliseconds, with a modest bandwidth requirement of 480KB.

Due to the significant development of the intelligence industry worldwide, various initiatives have increasingly recognized the value of the Internet of Things (IoT). IoT systems, however, are often hin- dered by fundamental challenges, such as the need for a central server to manage them. Decentralizing these systems can be achieved through the use of blockchains. Recently, there has been an increase in the popularity of blockchain in various fields, such as banking, IoT, and the intelligence industry, and human societies have taken notice of it. One of the main problems is with the scalability of such systems as the network size grows. This paper examines how to overcome this challenge in blockchain-based IoT systems. We introduce a sharding-based blockchain that is lightweight and scalable. In the proposed method, the nodes are assigned to a number of shards based on their history of activity. As part of this study, the Improved Byzantine Fault Tolerance with Graceful performance Degradation (IGDBFT) consensus algorithm is introduced within the proposed scheme for intra-shard consensus. A solution to storing blocks and cross-shard transactions has been developed using a global chain containing parent blocks in the cloud layer. Finally, we analyze the security and efficiency of our scheme and compare our sharding-based protocol with previous protocols.

As the global migration to post-quantum cryptography (PQC) continues to progress actively, in Korea, the Post-Quantum Cryptography Research Center has been established to acquire PQC technology, leading the KpqC Competition. In February 2022, the KpqC Competition issued a call for proposals for PQC algorithms. By November 2022, 16 candidates were selected for the first round (7 KEMs and 9 DSAs). Currently, Round 1 submissions are being evaluated with respect to security, efficiency, and scalability in various environments. At the current stage, evaluating the software through an analysis to improve the software quality of the first-round submissions is judged appropriately. In this paper, we present analysis results regarding performance and implementation security on based dependency-free approach of external libraries. Namely, we configure extensive tests for an analysis with no dependencies by replacing external libraries that can complicate the build process with hard coding. From the performance perspective, we provide analysis results of performance profiling, execution time, and memory usage for each of the KpqC candidates. From the implementation security perspective, we examine bugs and errors in the actual implementations using Valgrind software, a metamorphic testing methodology that can include wide test coverage and constant-time implementation against the timing attack. Until the KpqC standard algorithm is announced, we argue that continuous integration of extensive tests will lead to higher-level software quality of KpqC candidates.

Homomorphic Encryption (HE) is one of the mainstream cryptographic tools used to enable secure outsourced computation. A typical task is secure matrix computation. Popular HE schemes are all based on the problem of Ring Learning with Errors (RLWE), where the messages are encrypted in a ring. In general, the ring dimension should be large to ensure security, which is often larger than the matrix size. Hence, exploiting the ring structure to make fast homomorphic matrix computation has been an important topic in HE. In this paper, we present a new framework for encoding a matrix and performing multiplication on encrypted matrices. The new framework requires fewer basic homomorphic operations for matrix multiplication. Suppose that the ring dimension is $n$ and the matrix size is $d\times d$ with $d= n^{\rho}$. (1) In the compact case where $\rho \leq \frac{1}{3}$, the multiplication of two encrypted matrices requires $\tilde{O}(1)$ basic homomorphic operations, which include plaintext-ciphertext multiplications, ciphertext-ciphertext multiplications, and homomorphic Galois automorphisms. (2) In the large sized case where $\rho> \frac{1}{3}$, our new method requires $O\big(d^{(1 - \frac{1}{3\rho})\cdot \log_2 7 }\big)$ basic homomorphic operations, which is better than all existing methods. In addition, the new framework reduces the communication cost, since it requires fewer key-switching keys. The number of key-switching keys is reduced from $O(d)$ to $O(\log d)$.

When Satoshi Nakamoto introduced Bitcoin, a central tenet was that the blockchain functions as a timestamping server. In the Ethereum era, smart contracts widely assume on-chain timestamps are mostly accurate. In this paper, we prove this is indeed the case, namely that recorded timestamps do not wildly deviate from real-world time, a property we call timeliness. Assuming a global clock, we prove that all popular mechanisms for constructing blockchains (proof-of-work, longest chain proof-of-stake, and quorum-based proof-of-stake) are timely under honest majority, but a synchronous network is a necessary condition. Next we show that all timely blockchains can be suitably modified, in a black-box fashion, such that all honest parties output exactly the same ledgers at the same round, achieving a property we call supersafety, which may be of independent interest. Conversely, we also show that supersafety implies (perfect) timeliness, completing the circle.