Symmetric Private Information Retrieval (SPIR) is a protocol that protects privacy during data transmission. However, the existing SPIR focuses only on the privacy of the data to be requested on the server, without considering practical factors such as the payload that may be present during data transmission. This could seriously prevent SPIR from being applied to many complex data scenarios and hinder its further expansion. To solve such problems, we propose a primitive (PSKPIR) for symmetric private keyword information retrieval based on private set intersection (PSI) that supports payload transmission and batch keyword search. Specifically, we combine probe-and-XOR of strings (PaXoS) and Oblivious Programmable PRF (OPPRF) to construct PSI with payload (PSI-Payload) not only satisfies client privacy and server privacy, but also facilitates efficient payload transmission. The client can efficiently generate symmetric keys locally using keywords in the intersection, and receive payloads with matching labels in batches. In addition, we provide security definitions for PSKPIR and use the framework of universal composability (UC) to prove security. Finally, we implement PSKPIR with sublinear communication costs in both LAN and WAN settings. Experimental results show that our payload transfer speed is 10× faster than previous work on sufficiently large data sets.

We propose a novel obfuscation technique that can be used to outsource hard satisfiability (SAT) formulas to the cloud. Servers with large computational power are typically used to solve SAT instances that model real-life problems in task scheduling, AI planning, circuit verification and more. However, outsourcing data to the cloud may lead to privacy and information breaches since satisfying assignments may reveal considerable information about the underlying problem modeled by SAT. In this work, we develop CENSOR (privaCy prEserviNg obfuScation for Outsourcing foRmulas), a novel SAT obfuscation framework that resembles Indistinguishability Obfuscation. At the core of the framework lies a mechanism that transforms any formula to a random one with the same number of satisfying assignments. As a result, obfuscated formulas are indistinguishable from each other thus preserving the input-output privacy of the original SAT instance. Contrary to prior solutions that are rather adhoc in nature, we formally prove the security of our scheme. Additionally, we show that obfuscated formulas are within a polynomial factor of the original ones thus achieving polynomial slowdown. Finally, the whole process is efficient in practice, allowing solutions to original instances to be easily recovered from obfuscated ones. A byproduct of our method is that all NP problems can be potentially outsourced to the cloud by means of reducing to SAT.

The Lightning network (LN) addresses Bitcoin’s scalability issues by providing fast and private payment processing. In order to mitigate failures caused by insufficient channel capacities, LN introduced multi-path payments. To the best of our knowledge, the effect of multi-path payments remains unclear. In this paper, we therefore study the impact of multi-path payments on performance and privacy. We identify metrics quantifying the aforementioned properties and utilise them to evaluate the impact of multi-path payments. To this end, we develop a simulator implementing pathfinding in LN using single and multi-path payments as well as various pathfinding algorithms. We find that, while the success rate of multi-path payments is up to 20% higher, the impact of multi-path payments on performance otherwise remains within limits. On the other hand, the impact on privacy appears to be greater, e.g., multi-path payments are more likely to encounter an on-path adversary and the relationship anonymity is more likely to be compromised by colluding intermediate hops. However, multi-path payments are less likely to be deanonymised based on the path lengths.

We present Waffle, a datastore that protects an application’s data access patterns from a passive persistent adversary. Waffle achieves this without prior knowledge of the input data access distribution, making it the first of its kind to adaptively handle input sequences under a passive persistent adversary. Waffle maintains a constant bandwidth and client-side storage overhead, which can be adjusted to suit the application owner’s preferences. This flexibility allows the owner to fine-tune system parameters and strike a balance between security and performance. Our evaluation, utilizing the Yahoo! Cloud Serving Benchmark (YCSB) benchmark and Redis as the backend storage, demonstrates promising results. The insecure baseline outperforms Waffle by a mere 5-6x, whereas Waffle outperforms Pancake—a state-of-the-art oblivious datastore under passive persistent adversaries—by 45-57%, and a concurrent ORAM system, TaoStore, by 102x.

This paper centers around a simple yet crucial question for everyday users: How should one choose their delegated validators within proof-of-stake (PoS) protocols, particularly in the context of Ethereum 2.0? This has been a long-overlooked gap, as existing studies have primarily focused on inter-committee (validator set) behaviors and activities, while neglecting the dynamic formation of committees, especially for individual stakeholders seeking reliable validators. Our study bridges this gap by diving into the delegation process (normal users delegate their small-value tokens to delegatees who later act as validators) before entering an actual consensus phase. We propose a Bayesian model to quantify normal users' trust in delegatees, which we further incorporate into a game-theoretical model to simulate users' reactions against a set of critical factors identified through extensive research (including 10+ staking service providers as well as 30+ PoS blockchains). Our results reveal that users tend to choose their delegatees and utilize their tokens by carefully weighing the delegation cost, the behaviors of other users, and the reputation of delegatees, ultimately reaching a Nash equilibrium. Unfortunately, the collective trend significantly increases the likelihood of token concentration on a small number of delegatees.

In invitation-based systems, a new user can register upon having a threshold number of invitations issued by the existing members. The newcomer hands his invitations to the system administrator who verifies whether the invitations are issued by legitimate members. This causes the administrator to be aware of who is invited by whom. However, the inviter-invitee relationship is privacy-sensitive information and can lead to inference attacks where the invitee's profile (e.g., political view or location) can be extracted through the profiles of his inviters. Addressing this problem, we propose Anonyma, an anonymous invitation-based system, where a corrupted administrator, who may even collude with a subset of existing members, is not able to figure out who is invited by whom. We formally define and prove the inviter anonymity as well as unforgeability of invitations against a malicious and adaptive adversary. Our design only incurs a constant cost to authenticate a new registration. This is significantly better than similar works where the generation of invitations and verification of new registration cause an overhead linear in the total number of existing members. Besides, Anonyma is efficiently scalable in the sense that once a user joins the system, the administrator can instantly, and without re-keying the existing members, issue credentials for the newcomer to be able to act as an inviter. We additionally design Anonymax, an anonymous cross-network invitation-based system empowering third-party authentication where the invitations issued by the members of one system can be used for registering to another system.

Account recovery enables users to regain access to their accounts when they lose their authentication credentials. While account recovery is well established and extensively studied in the Web2 (traditional web) context, Web3 account recovery presents unique challenges. In Web3, accounts rely on a (cryptographically secure) private-public key pair as their credential, which is not expected to be shared with a single entity like a server owing to security concerns. This makes account recovery in the Web3 world distinct from the Web2 landscape, often proving to be challenging or even impossible. As account recovery has proven crucial for Web2 authenticated systems, various solutions have emerged to address account recovery in the Web3 blockchain ecosystem in order to make it more friendly and accessible to everyday users, without "punishing" users if they make honest mistakes. This study systematically examines existing account recovery solutions within the blockchain realm, delving into their workflows, underlying cryptographic mechanisms, and distinct characteristics. After highlighting the trilemma between usability, security, and availability encountered in the Web3 recovery setting, we systematize the existing recovery mechanisms across several axes which showcase those tradeoffs. Based on our findings, we provide a number of insights and future research directions in this field.

Secure inference of deep convolutional neural networks (CNNs) under RNS-CKKS involves polynomial approximation of unsupported non-linear activation functions. However, existing approaches have three main limitations: 1) Inflexibility: The polynomial approximation and associated homomorphic evaluation architecture are customized manually for each CNN architecture and do not generalize to other networks. 2) Suboptimal Approximation: Each activation function is approximated instead of the function represented by the CNN. 3) Restricted Design: Either high-degree or low-degree polynomial approximations are used. The former retains high accuracy but slows down inference due to bootstrapping operations, while the latter accelerates ciphertext inference but compromises accuracy. To address these limitations, we present AutoFHE, which automatically adapts standard CNNs for secure inference under RNS-CKKS. The key idea is to adopt layerwise mixed-degree polynomial activation functions, which are optimized jointly with the homomorphic evaluation architecture in terms of the placement of bootstrapping operations. The problem is modeled within a multi-objective optimization framework to maximize accuracy and minimize the number of bootstrapping operations. AutoFHE can be applied flexibly on any CNN architecture, and it provides diverse solutions that span the trade-off between accuracy and latency. Experimental evaluation over RNS-CKKS encrypted CIFAR datasets shows that AutoFHE accelerates secure inference by $1.32\times$ to $1.8\times$ compared to methods employing high-degree polynomials. It also improves accuracy by up to 2.56% compared to methods using low-degree polynomials. Lastly, AutoFHE accelerates inference and improves accuracy by $103\times$ and 3.46%, respectively, compared to CNNs under TFHE.

As the ubiquity and complexity of system-on-chip (SoC) designs increase across electronic devices, the task of incorporating security into an SoC design flow poses significant challenges. Existing security solutions are inadequate to provide effective verification of modern SoC designs due to their limitations in scalability, comprehensiveness, and adaptability. On the other hand, Large Language Models (LLMs) are celebrated for their remarkable success in natural language understanding, advanced reasoning, and program synthesis tasks. Recognizing an opportunity, our research delves into leveraging the emergent capabilities of Generative Pre-trained Transformers (GPTs) to address the existing gaps in SoC security, aiming for a more efficient, scalable, and adaptable methodology. By integrating LLMs into the SoC security verification paradigm, we open a new frontier of possibilities and challenges to ensure the security of increasingly complex SoCs. This paper offers an in-depth analysis of existing works, showcases practical case studies, demonstrates comprehensive experiments, and provides useful promoting guidelines. We also present the achievements, prospects, and challenges of employing LLM in different SoC security verification tasks.

In the rapidly advancing domain of artificial intelligence, ChatGPT, powered by the GPT-4 model, has emerged as a state-of-the-art interactive agent, exhibiting substantial capabilities across various domains. This paper aims to assess the efficacy of GPT-4 in addressing and solving problems found within cryptographic examinations. We devised a multi-faceted methodology, presenting the model with a series of cryptographic questions of varying complexities derived from real academic examinations. Our evaluation encompasses both classical and modern cryptographic challenges, focusing on the model's ability to understand, interpret, and generate correct solutions while discerning its limitations. The model was challenged with a spectrum of cryptographic tasks, earning 201 out of 208 points by solving fundamental queries inspired by an oral exam, 80.5 out of 90 points on a written Crypto 1 exam, and 287 out of 385 points on advanced exercises from the Crypto 2 course. The results demonstrate that while GPT-4 shows significant promise in grasping fundamental cryptographic concepts and techniques, certain intricate problems necessitate domain-specific knowledge that may sometimes lie beyond the model's general training. Insights from this study can provide educators, researchers, and examiners with a deeper understanding of how cutting-edge AI models can be both an asset and a potential concern in academic settings related to cryptology. To enhance the clarity and coherence of our work, we utilized ChatGPT-4 to help us in formulating sentences in this paper.

As a fundamental operation in fixed-point arithmetic, truncation can bring the product of two fixed-point integers back to the fixed-point representation. In large-scale applications like privacy-preserving machine learning, it is essential to have faithful truncation that accurately eliminates both big and small errors. In this work, we improve and extend the results of the oblivious transfer based faithful truncation protocols initialized by Cryptflow2 (Rathee et al., CCS 2020). Specifically, we propose a new notion of two-bit extraction that is tailored for faithful truncation and demonstrate how it can be used to construct an efficient faithful truncation protocol. Benefiting from our efficient construction for two-bit extraction, our faithful truncation protocol reduces the communication complexity of Cryptflow2 from growing linearly with the fixed-point precision to logarithmic complexity. This efficiency improvement is due to the fact that we reuse the intermediate results of eliminating the big error to further eliminate the small error. Our reuse strategy is effective, as it shows that while eliminating the big error, it is possible to further eliminate the small error at a minimal cost, e.g., as low as communicating only an additional 160 bits in one round.

Payment channel networks (PCNs) are a layer-2 blockchain scalability solution, with its main entity, the payment channel, enabling transactions between pairs of nodes "off-chain," thus reducing the burden on the layer-1 network. Nodes with multiple channels can serve as relays for multihop payments by providing their liquidity and withholding part of the payment amount as a fee. Relay nodes might after a while end up with one or more unbalanced channels, and thus need to trigger a rebalancing operation. In this paper, we study how a relay node can maximize its profits from fees by using the rebalancing method of submarine swaps. We introduce a stochastic model to capture the dynamics of a relay node observing random transaction arrivals and performing occasional rebalancing operations, and express the system evolution as a Markov Decision Process. We formulate the problem of the maximization of the node's fortune over time over all rebalancing policies, and approximate the optimal solution by designing a Deep Reinforcement Learning (DRL)-based rebalancing policy. We build a discrete event simulator of the system and use it to demonstrate the DRL policy's superior performance under most conditions by conducting a comparative study of different policies and parameterizations. Our work is the first to introduce DRL for liquidity management in the complex world of PCNs.

In our survey of the various zk-EVM constructions, it becomes apparent that verifiable storage of the EVM state starts to be one of the dominating costs. This is not surprising because a big differentiator of EVM from UTXO is exactly the ability to carry states and, most importantly, their transitions; i.e., EVM is a **state** machine. In other words, to build an efficient zk-EVM, one must first build an efficient verifiable state. The common approach, which has been used in production, is a Merkle forest to authenticate the memory that would be randomly accessed within zk-SNARK, and optimize the verification of such memory accesses. In this note, we describe a way to instantiate a Merkle tree with very few gates in TurboPlonk. We use customized gates in TurboPlonk to implement a SNARK-friendly hash function called Anemoi and its Jive mode of operation, by Clémence Bouvier, Pierre Briaud, Pyrros Chaidos, Léo Perrin, Robin Salen, Vesselin Velichkov, and Danny Willems. We demonstrate that with $16$ gates ($\approx1$ gate per round in a 14-round Amenoi hash), one can verify a 3-to-1 compression in a 3-ary Merkle tree. Before this, prior implementations would often require hundreds of gates. We anticipate this technique to benefit a large number of applications built off zk-SNARK. Our code can be found in $\mathtt{noah}$: https://github.com/FindoraNetwork/noah

Popular Ethereum wallets (like MetaMask) entrust centralized infrastructure providers (e.g., Infura) to run the consensus client logic on their behalf. As a result, these wallets are light-weight and high-performant, but come with security risks. A malicious provider can mislead the wallet by faking payments and balances, or censoring transactions. On the other hand, light clients, which are not in popular use today, allow decentralization, but are concretely inefficient, often with asymptotically \emph{linear} bootstrapping complexity. This poses a dilemma between decentralization and performance. We design, implement, and evaluate a new proof-of-stake (PoS) \emph{superlight} client with concretely efficient and asymptotically \emph{logarithmic} bootstrapping complexity. Our proofs of proof-of-stake (PoPoS) take the form of a Merkle tree of PoS epochs. The verifier enrolls the provers in a bisection game, in which honest provers are destined to win once an adversarial Merkle tree is challenged at sufficient depth. We provide an implementation for mainnet Ethereum: compared to the state-of-the-art light client construction of Ethereum, our client improves time-to-completion by $9\times$, communication by $180\times$, and energy usage by $30\times$ (when bootstrapping after $10$ years of consensus execution). As an important additional application, our construction can be used to realize trustless cross-chain bridges, in which the superlight client runs within a smart contract and takes the role of an on-chain verifier. We prove our construction is secure and show how to employ it for other PoS systems such as Cardano (with fully adaptive adversary), Algorand, and Snow White.

In [1], Sarier presents a practical biometric-based non-transferable credential scheme that maintains the efficiency of the underlying Brands credential. In this paper, we design a new Blockchain-Based E-Voting (BBEV) scheme that combines the system of [1] with encrypted Attribute Based Credentials for a non-transferable code-voting approach to achieve efficient, usable, anonymous, transparent, auditable, verifiable, receipt-free and coercion-resistant remote voting system for small/medium scale elections. To the best of our knowledge, the system is the first user-centric BBEV scheme that depends on the one-show Brands' Credential both for biometric authentication and pre-encrypted ballot generation leading to a natural prevention against double voting. Even though the system is instantiated with Bitcoin Blockchain due to its prevalence and various coin mixers available for anonymity, the system is designed to be generic, i.e. independent of the cryptocurrency. Thus, the new BBEV scheme can be extended to large-scale elections for public Blockchains with higher throughput/cheaper transaction fees. Finally, a cost analysis based on the last USA presidential election data shows that, the new BBEV is advantageous over the traditional one if implemented for three consecutive elections.

Central banks around the world are actively exploring the issuance of retail central bank digital currency (rCBDC), which is widely seen as a key upgrade of the monetary system in the 21st century. However, privacy concerns are the main impediment to rCBDC’s development and roll-out. A central bank as the issuer of rCBDC would typically need to keep a digital ledger to record all the balances and transactions of citizens. These data, when combined with other data, could possibly disclose the spending habits of all citizens. On the one hand, the eligible rights of people to keep their transactions private should be protected, including against central bank surveillance. On the other hand, the central bank needs to ensure that no over-issuance of money or other frauds occur, necessarily demanding a certain form of knowledge of rCBDC transactions to safeguard against malicious users who create counterfeit money or spend duplicated money. This work investigates cryptographic tools and privacy-enhancing technology with the aim to craft a scalable solution to strike a balance between user privacy and transaction verifiability. Different from the current mainstream thought among central banks, it assumes that the central bank maintains a ledger to record all balances and transactions of citizens, but in a concealed form. Specifically, this work focuses on rCBDC architectures based on the unspent transaction output (UTXO) data model and tackles the research problem of preserving a sufficient degree of privacy for UTXO transaction records while allowing the central bank to verify their correctness. While UTXO-based rCBDC architectures were widely tested among major central banks, user privacy is not adequately addressed. The adoption of evolving public keys as pseudonyms to hide the real identities of users is the most advanced privacy design for UTXO-based rCBDC, but it only solves the privacy issue partially. Some information could still be leaked out. This work investigates techniques to address the shortcomings of the pseudonym approach. First, a Pedersen commitment scheme is applied to hide the transaction values of a UTXO transaction while allowing the central bank to verify that no over-issuance of rCBDC has occurred in the transaction. Contrary to the conventional approach, which applies a zero knowledge proof to prove no over-issuance, this work uses a Schnorr signature. This not only reduces the overheads but also enables a non-interactive proof. Then, Coinjoin is applied to aggregate UTXO transactions from different users into one larger UTXO transaction to obfuscate the payer-payee relationship while preserving the correctness of the amount of money flow. This work applies a well-developed notion in database research, namely, k-anonymity, to analyse the privacy guarantee of Coinjoin. Through modelling the transaction traffic by a Poisson process, the trade-off between anonymity and transaction confirmation time of Coinjoin is analysed.

TEE-based smart contracts are an emerging blockchain architecture, offering fully programmable privacy with better performance than alternatives like secure multiparty computation. They can also support compatibility with existing smart contract languages, such that existing (plaintext) applications can be readily ported, picking up privacy enhancements automatically. While previous analysis of TEE-based smart contracts have focused on failures of TEE itself, we asked whether other aspects might be understudied. We focused on state consistency, a concern area highlighted by Li et al., as well as new concerns including access pattern leakage and software upgrade mechanisms. We carried out a code review of a cohort of four TEE-based smart contract platforms. These include Secret Network, the first to market with in-use applications, as well as Oasis, Phala, and Obscuro, which have at least released public test networks. The first and most broadly applicable result is that access pattern leakage occurs when handling persistent contract storage. On Secret Network, its fine-grained access pattern is catastrophic for the transaction privacy of SNIP-20 tokens. If ERC-20 tokens were naively ported to Oasis they would be similarly vulnerable; the others in the cohort leak coarse-grained information at approximately the page level (4 kilobytes). Improving and characterizing this will require adopting techniques from ORAMs or encrypted databases. Second, the importance of state consistency has been under-appreciated, in part because exploiting such vulnerabilities is thought to be impractical. We show they are fully practical by building a proof-of-concept tool that breaks all advertised privacy properties of SNIP-20 tokens, able to query the balance of individual accounts and the token amount of each transfer. We additionally demonstrate MEV attacks against the Sienna Swap application. As a final consequence of lacking state consistency, the developers have inadvertently introduced a decryption backdoor through their software upgrade process. We have helped the Secret developers mitigate this through a coordinated vulnerability disclosure, after which their state consistency should be roughly on par with the rest.

Resource efficiency in blockchain systems remains a pivotal concern in their design. While Ethereum often experiences network congestion, leading to rewarding opportunities for miners through transaction inclusions, a significant amount of block space remains underutilized. Remarkably, instances of entirely unutilized blocks contribute to resource wastage within the Ethereum ecosystem. This study delves into the incentives driving miners to produce empty blocks. We ascertain that the immediate rewards of mining empty blocks often lead miners to forego potential benefits from transaction inclusions. Moreover, our investigation reveals a marked reduction in empty blocks after the Ethereum's Merge, highlighting that the Proof-of-Stake (PoS) consensus mechanism enhances block space efficiency in the blockchain sphere.

In this paper we present PQ-WireGuard, a post-quantum variant of the handshake in the WireGuard VPN protocol (NDSS 2017). Unlike most previous work on post-quantum security for real-world protocols, this variant does not only consider post-quantum confidentiality (or forward secrecy) but also post-quantum authentication. To achieve this, we replace the Diffie-Hellman-based handshake by a more generic approach only using key-encapsulation mechanisms (KEMs). We establish security of PQ-WireGuard, adapting the security proofs for WireGuard in the symbolic model and in the standard model to our construction. We then instantiate this generic construction with concrete post-quantum secure KEMs, which we carefully select to achieve high security and speed. We demonstrate competitiveness of PQ-WireGuard presenting extensive benchmarking results comparing to widely deployed VPN solutions.

Count-Min Sketch (CMS) and HeavyKeeper (HK) are two realizations of a compact frequency estimator (CFE). These are a class of probabilistic data structures that maintain a compact summary of (typically) high-volume streaming data, and provides approximately correct estimates of the number of times any particular element has appeared. CFEs are often the base structure in systems looking for the highest-frequency elements (i.e., top-$K$ elements, heavy hitters, elephant flows). Traditionally, probabilistic guarantees on the accuracy of frequency estimates are proved under the implicit assumption that stream elements do not depend upon the internal randomness of the structure. Said another way, they are proved in the presence of data streams that are created by non-adaptive adversaries. Yet in many practical use-cases, this assumption is not well-matched with reality; especially, in applications where malicious actors are incentivized to manipulate the data stream. We show that the CMS and HK structures can be forced to make significant estimation errors, by concrete attacks that exploit adaptivity. We analyze these attacks analytically and experimentally, with tight agreement between the two. Sadly, these negative results seem unavoidable for (at least) sketch-based CFEs with parameters that are reasonable in practice. On the positive side, we give a new CFE (Count-Keeper) that can be seen as a composition of the CMS and HK structures. Count-Keeper estimates are typically more accurate (by at least a factor of two) than CMS for ``honest" streams; our attacks against CMS and HK are less effective (and more resource intensive) when used against Count-Keeper; and Count-Keeper has a native ability to flag estimates that are suspicious, which neither CMS or HK (or any other CFE, to our knowledge) admits.

Blockchain users who submit transactions through private pools are guaranteed pre-trade privacy but face execution risk. We argue that private pools serve the intended purpose of eliminating frontrunning risk, only if such risk is high. Otherwise, some validators may decide to avoid monitoring private pools to preserve rents extracted from frontrunning bots. Private pools intensify the execution arms race for bots, thus decreasing their payoffs {and increasing validators' rents}. The private pool option reduces blockspace allocative inefficiencies and raises aggregate welfare.

An anonymous communication network (ACN) is designed to protect the identities of two parties communicating through it, even if an adversary controls or observes parts of the network. Among the ACNs, Tor represents a practical trade-off between offering a reasonable level of anonymity and, simultaneously, an acceptable transmission delay. Due to its practical impact, there is abundant literature on the performance of Tor concerning both communication and security aspects. Recently, a static framework was suggested for evaluating and comparing, in a quantifiable way, the effect of different scenarios (attacks, defence mechanisms, and other protocol changes). Although a static model is useful, many scenarios involve parameters and stochastic variables that change or evolve over time, or that may be influenced by active and malicious adversaries. In this paper, we propose a dynamic framework for evaluating such scenarios. We identify several scenarios where this framework is applicable, and illustrate our framework by considering the guard node mechanism in Tor. We evaluate and compare variations on the guard node concept suggested in the literature with respect to relevant performance metrics and, using the framework, support our evaluation with a theoretical analysis.

Homomorphic Encryption (HE) presents a promising solution to securing neural networks for Machine Learning as a Service (MLaaS). Despite its potential, the real-time applicability of current HE-based solutions remains a challenge, and the diversity in network structures often results in inefficient implementations and maintenance. To address these issues, we introduce a unified and compact network structure for real-time inference in convolutional neural networks based on HE. We further propose several optimization strategies, including an innovative compression and encoding technique and rearrangement in the pixel encoding sequence, enabling a highly efficient batched computation and reducing the demand for time-consuming HE operations. To further expedite computation, we propose a GPU acceleration engine to leverage the massive thread-level parallelism to speed up computations. We test our framework with the MNIST, Fashion-MNIST, and CIFAR-10 datasets, demonstrating accuracies of 99.14%, 90.8%, and 61.09%, respectively. Furthermore, our framework maintains a steady processing speed of 0.46 seconds on a single-thread CPU, and a brisk 31.862 milliseconds on an A100 GPU for all datasets. This represents an enhancement in speed more than 3000 times compared to pervious work, paving the way for future explorations in the realm of secure and real-time machine learning applications.

Secure multi-party computation (MPC) enables multiple distrusting parties to compute a function while keeping their respective inputs private. In a threshold implementation of a symmetric primitive, e.g., of a block cipher, each party holds a share of the secret key or of the input block. The output block is computed without reconstructing the secret key. This enables the construction of distributed TPMs or transciphering for secure data transmission in/out of the MPC context. This paper investigates implementation approaches for the lightweight primitives SKINNY and PHOTON in arithmetic circuits. For these primitives, we identify arithmetic expressions for the S-box that result in smaller arithmetic circuits compared to the Boolean expressions from the literature. We validate the optimization using a generic actively secure MPC protocol and obtain 18% faster execution time with 49% less communication data for SKINNY-64-128 and 27% to 74% faster execution time with 49% to 81% less data for PHOTON $P_{100}$ and $P_{288}$. Furthermore, we find a new set of parameters for the heuristic method of polynomial decomposition, introduced by Coron, Roy and Vivek, specialized for SKINNY's 8-bit S-box. We reduce the multiplicative depth from 9 to 5.

Amid the landscape of confidential computing, where security and privacy reign supreme, PRIVATON emerges as a pioneering and practical solution to safeguard sensitive data and computations. A verifiable proof of computation model, with one of its variant built upon the dual sandbox strategy, PRIVATON combines Trusted Execution Environment (TEE) technologies with WebAssembly (WASM) runtime environments to establish an ecosystem for privacy-preserving computations. This approach involves fine grained modeling of computations as finite state automatons, guided by verifiable proofs that attest to their unerring execution. PRIVATON is guided by the profound principles of "least privilege" and "intentional use." Through the former, each computation module's privileges are meticulously constrained, reducing vulnerability vectors. The latter ensures that privileges are allocated explicitly, fostering comprehension and security. This rigorous adherence minimizes privilege misuse and information leakage, bolstering the overall security posture. At its core, PRIVATON's innovation lies in its comprehensive assurance of data privacy and security. State machine proofs not only attest to the absence of data leakage but also prevent unauthorized data transmission. By providing unassailable proof of computation integrity, PRIVATON shields against code misuse within the system. This proactive stance fortifies its mission to safeguard the sanctity of data, computations, and the privacy of all stakeholders. Evidenced by its real-world application, PRIVATON has been validated within the cryptocurrency trading ecosystem, where it acts as a distributed and privacy-preserving credit oracle. Its implementation within Credora’s landscape underlines its potential to transform data-centric paradigms, empowering stakeholders with an unshakeable confidence in data security. In a world where data privacy is paramount, PRIVATON stands as a guardian, epitomizing the convergence of technology, security, and trust.

In this paper we address the problem of recovery from failures without re-running entire elections when elections fail to verify. We consider the setting of $\textit{dual voting}$ protocols, where the cryptographic guarantees of end-to-end verifiable voting (E2E-V) are combined with the simplicity of audit using voter-verified paper records (VVPR). We first consider the design requirements of such a system and then suggest a protocol called $\textit{OpenVoting}$, which identifies a verifiable subset of error-free votes consistent with the VVPRs, and the polling booths corresponding to the votes that fail to verify with possible reasons for the failures.

In this paper, we present the Webb Protocol, a system for building and governing cross-chain applications that can support shared anonymity set functionality across a set of identical bridged systems on compatible blockchains. The Webb Protocol is composed of two major protocols that deal with storing, updating, and validating of data and state changes that occur on a bridge and that are relevant to replicate on each connected chain. State is efficiently verifiable through the use of merkle trees and privacy is provided using zero-knowledge proofs of membership. Together, one can create applications leveraging distributed state with private property testing capabilities. Both financial and non-financial applications are described as motivating examples within the paper.

This article presents three retrospective case studies of cryptography-related flaws in censorship circumvention protocols: a decryption oracle in Shadowsocks “stream cipher” methods, non-uniform Elligator public key representatives in obfs4, and a replay-based active distinguishing attack exploiting malleability in VMess. These three protocols come from the family of “fully encrypted” circumvention protocols: their traffic in both directions is indistinguishable from a uniformly random stream of bytes (or at least, is supposed to be). Some of the flaws are fixable implementation errors; others are rooted in more fundamental design errors. Their consequences range from enabling passive probabilistic detection to complete loss of confidentiality. All have been fixed, mitigated, or superseded since their discovery. My primary purpose is to provide an introduction of circumvention threat models to specialists in cryptography, and to make the point that while cryptography is a necessary tool in circumvention, it is not the sole or even most important consideration. Secondarily, I want to furnish a few instructive examples of cryptographic design and implementation errors in uncontrived, deployed protocols. While the flaws I discuss affected systems of significant social importance with millions of collective users, they are not well-known outside a small circle of specialists in circumvention.

Over the last decade, online reputation has become a central aspect of our digital lives. Most online services and communities assign a reputation score to users, based on feedback from other users about various criteria such as how reliable, helpful, or knowledgeable a person is. While many online services compute reputation based on the same set of such criteria, users currently do not have the ability to use their reputation scores across services. As a result, users face trouble establishing themselves on new services or trusting each other on services that do not support reputation tracking. Existing systems that aggregate reputation scores, unfortunately, provide no guarantee in terms of user privacy, and their use makes user accounts linkable. Such a lack of privacy may result in embarrassment, or worse, place users in danger. In this paper, we present StreetRep, a practical system for aggregating user reputation scores in a privacy-preserving manner. StreetRep makes it possible for users to provide their aggregated scores over multiple services without revealing their respective identities on each service. We discuss our novel approach for tamper-proof privacy preserving score aggregation from multiple sources by combining existing techniques such as blind signatures, homomorphic signatures and private information retrieval. We discuss its practicality and resiliency against different types of attacks. We also built a prototype implementation of StreetRep. Our evaluation demonstrates that StreetRep (a) performs efficiently and (b) practically scales to a large user base.

We introduce Lanturn: a general purpose adaptive learning-based framework for measuring the cryptoeconomic security of composed decentralized-finance (DeFi) smart contracts. Lanturn discovers strategies comprising of concrete transactions for extracting economic value from smart contracts interacting with a particular transaction environment. We formulate the strategy discovery as a black-box optimization problem and leverage a novel adaptive learning-based algorithm to address it. Lanturn features three key properties. First, it needs no contract-specific heuristics or reasoning, due to our black-box formulation of cryptoeconomic security. Second, it utilizes a simulation framework that operates natively on blockchain state and smart contract machine code, such that transactions returned by Lanturn’s learning-based optimization engine can be executed on-chain without modification. Finally, Lanturn is scalable in that it can explore strategies comprising a large number of transactions that can be reordered or subject to insertion of new transactions. We evaluate Lanturn on the historical data of the biggest and most active DeFi Applications: Sushiswap, UniswapV2, UniswapV3, and AaveV2. Our results show that Lanturn not only rediscovers existing, well-known strategies for extracting value from smart contracts, but also discovers new strategies that are previously undocumented. Lanturn also consistently discovers higher value than evidenced in the wild, surpassing a natural baseline computed using value extracted by bots and other strategic agents.

We introduce the first practical protocols for fully decentralized sealed-bid auctions using timed commitments. Timed commitments ensure that the auction is finalized fairly even if all participants drop out after posting bids or if $n-1$ bidders collude to try to learn the $n^{th}$ bidder’s bid value. Our protocols rely on a novel non-malleable timed commitment scheme which efficiently supports range proofs to establish that bidders have sufficient funds to cover a hidden bid value. This allows us to penalize users who abandon bids for exactly the bid value, while supporting simultaneous bidding in multiple auctions with a shared collateral pool. Our protocols are concretely efficient and we have implemented them in an Ethereum- compatible smart contract which automatically enforces payment and delivery of an auctioned digital asset.

Payment channel hubs (PCHs) serve as a promising solution to achieving quick off-chain payments between pairs of users. They work by using an untrusted tumbler to relay the payments between the payer and payee and enjoy the advantages of low cost and high scalability. However, the most recent privacy-preserving payment channel hub solution that supports variable payment amounts suffers from limited unlinkability, e.g., being vulnerable to the abort attack. Moreover, this solution utilizes non-interactive zero-knowledge proofs, which bring huge costs on both computation time and communication overhead. Therefore, how to design PCHs that support variable amount payments and unlinkability, but reduce the use of huge-cost cryptographic tools as much as possible, is significant for the large-scale practical applications of off-chain payments. In this paper, we propose Accio, a variable amount payment channel hub solution with optimized unlinkability, by deepening research on unlinkability and constructing a new cryptographic tool. We provide the detailed Accio protocol and formally prove its security and privacy under the Universally Composable framework. Our prototype demonstrates its feasibility and the evaluation shows that Accio outperforms the other state-of-the-art works in both communication and computation costs.

The global supply chain involves multiple independent entities, and potential adversaries can exploit different attack vectors to steal proprietary designs and information. As a result, intellectual property (IP) owners and consumers have reasons to keep their designs private. Without a trusted third party, this mutual mistrust can lead to a deadlock where IP owners are unwilling to disclose their IP core before a financial agreement is reached, while consumers need assurance that the proprietary design will meet their integration needs without compromising the confidentiality of their test vectors. To address this challenge, we introduce an efficient framework called MPloC that resolves this deadlock by allowing owners and consumers to jointly evaluate the target design with consumer-supplied test vectors while preserving the privacy of both the IP core and the inputs. MPloC is the first work that combines secure multiparty computation (MPC) and logic-locking techniques to accomplish these goals. Our approach supports both semi-honest and malicious security models to allow users to balance stronger security guarantees with performance. We compare our approach to existing state-of-the-art works that utilize homomorphic encryption across several benchmarks and report runtime improvements of more than two orders of magnitude.

Vehicle-to-grid (V2G) networks, as an emerging smart grid paradigm, can be integrated with renewable energy resources to provide power services and manage electricity demands. When accessing electricity services, an electric vehicle(EV) typically provides authentication or/and payment information containing identifying data to a service provider, which raises privacy concerns as malicious entities might trace EV activity or exploit personal information. Although numerous anonymous authentication and payment schemes have been presented for V2G networks, no such privacy-preserving scheme supports authentication and payment simultaneously. Therefore, this paper is the first to present a privacy-preserving authentication scheme with anonymous payment for V2G networks (PAP, for short). In addition, this scheme also supports accountability and revocability, which are practical features to prevent malicious behavior; minimal attribute disclosure, which maximizes the privacy of EV when responding to the service provider's flexible access policies; payment binding, which guarantees the accountability in the payment phase; user-controlled linkability, which enables EV to decide whether different authentication sessions are linkable for continuous services. On the performance side, we implement PAP with the pairing cryptography library, then evaluate it on different hardware platforms, showing that it is essential for V2G applications.

Cryptocurrency exchange platforms such as Coinbase, Binance, enable users to purchase and sell cryptocurrencies conveniently just like trading stocks/commodities. However, because of the nature of blockchain, when a user withdraws coins (i.e., transfers coins to an external on-chain account), all future transactions can be learned by the platform. This is in sharp contrast to conventional stock exchange where all external activities of users are always hidden from the platform. Since the platform knows highly sensitive user private information such as passport number, and bank information, linking all (on-chain) transactions raises a serious privacy concern about the potential disastrous data breach in those cryptocurrency exchange platforms. In this paper, we propose a cryptocurrency exchange that restores user anonymity for the first time. To our surprise, the seemingly well-studied privacy/anonymity problem has several new challenges in this setting. Since the public blockchain and internal transaction activities naturally provide many non-trivial leakages to the platform, internal privacy is not only useful in the usual sense but also becomes necessary for regaining the basic anonymity of user transactions. We also ensure that the user cannot double spend, and the user has to properly report accumulated profit for tax purposes, even in the private setting. We give a careful modeling and efficient construction of the system that achieves constant computation and communication overhead (with only simple cryptographic tools and rigorous security analysis); we also implement our system and evaluate its practical performance.

This work formally analyzes the anonymity guarantees of continuous stop-and-go mixnets and attempts to answer the above question. Existing mixnet based anonymous communication protocols that aim to provide provable anonymity guarantees rely on round-based communication models --- which requires synchronization among all the nodes and clients, and difficult to achieve in practice. Continuous stop-and-go mixnets (e.g., Loopix and Nym) provide a nice alternative by adding a random delay for each message on every hop independent of all other hops and all other messages. The core anonymization technique of continuous mixnets combined with the fact that the messages are sent by the clients to the mixnet at different times makes it a difficult problem to formally prove security for such mixnet protocols; all existing analyses for such designs provide only experimental evaluations for anonymity. We are the first to close that gap and provide a formal analysis. We provide two indistinguishability based definitions (of sender anonymity), namely pairwise unlinkability and user unlinkability, tuned specifically for continuous stop-and-go mixnets. We derive the adversarial advantage as a function of the protocol parameters for the two definitions. We show that there is a fundamental lower bound on the adversarial advantage $\delta$ for pairwise unlinkability; however, strong user unlinkability (negligible adversarial advantage) can be achieved if the users message rate ($\lambda_u$) is proportional to message processing rate ($\lambda$) on the nodes.

Uniswap is currently the most liquid Decentralized Exchange (DEX) on Ethereum. In May 2021, it upgraded to the third protocol version named Uniswap V3. The key feature update is “concentrated liquidity”, which supports liquidity provision within custom price ranges. However, this design introduces a new type of Miner Extractable Value (MEV) source called Just-in-Time (JIT) liquidity attack, where the adversary mints and burns a liquidity position right before and after a sizable swap. We begin by formally defining the JIT liquidity attack and subsequently conduct empirical measurements on Ethereum. Over a span of 20 months, we identify 36,671 such attacks, which have collectively generated profits of 7,498 ETH. Our analysis suggests that the JIT liquidity attack essentially represents a whales’ game, predominantly controlled by a select few bots. The most active bot, identified as 0xa57...6CF, has managed to amass 92% of the total profit. Furthermore, we find that this attack strategy poses significant entry barriers, as it necessitates adversaries to provide liquidity that is, on average, 269 times greater than the swap volume. In addition, our findings reveal that the JIT liquidity attack exhibits relatively poor profitability, with an average Return On Investment (ROI) of merely 0.007%. We also find this type of attack to be detrimental to existing Liquidity Providers (LPs) within the pool, as their shares of liquidity undergo an average dilution of 85%. On the contrary, this attack proves advantageous for liquidity takers, who secure execution prices that are, on average, 0.139% better than before. We further dissect the behaviors of the top MEV bots and evaluate their strategies through local simulation. Our observations reveal that the most active bot, 0xa57...6CF, conducted 27% of non-optimal attacks, thereby failing to capture at least 7,766 ETH (equivalent to 16.1M USD) of the potential attack profit.

Homomorphic encryption (HE) enables calculating on encrypted data, which makes it possible to perform privacy- preserving neural network inference. One disadvantage of this technique is that it is several orders of magnitudes slower than calculation on unencrypted data. Neural networks are commonly trained using floating-point, while most homomorphic encryption libraries calculate on integers, thus requiring a quantisation of the neural network. A straightforward approach would be to quantise to large integer sizes (e.g., 32 bit) to avoid large quantisation errors. In this work, we reduce the integer sizes of the networks, using quantisation-aware training, to allow more efficient computations. For the targeted MNIST architecture proposed by Badawi et al., we reduce the integer sizes by 33% without significant loss of accuracy, while for the CIFAR architecture, we can reduce the integer sizes by 43%. Implementing the resulting networks under the BFV homomorphic encryption scheme using SEAL, we could reduce the execution time of an MNIST neural network by 80% and by 40% for a CIFAR neural network.

DuckyZip is a provably honest global linking service which links short memorable identifiers to arbitrarily large payloads (URLs, text, documents, archives, etc.) without being able to undetectably provide different payloads for the same short identifier to different parties. DuckyZip uses a combination of Verifiable Random Function (VRF)-based zero knowledge proofs and a smart contract in order to provide strong security guarantees: despite the transparency of the smart contract log, observers cannot feasibly create a mapping of all short identifiers to payloads that is faster than $\mathcal{O}(n)$ classical enumeration.

The emergence of blockchain technologies as central components of financial frameworks has amplified the extraction of market inefficiencies, such as arbitrage, through Miner Extractable Value (MEV) from Decentralized Finance smart contracts. Exploiting these opportunities often requires fee payment to miners and validators, colloquially termed as bribes. The recent development of centralized MEV relayers has led to these payments shifting from the public transaction pool to private channels, with the objective of mitigating information leakage and curtailing execution risk. This transition instigates highly competitive first-price auctions for MEV. However, effective bidding strategies for these auctions remain unclear. This paper examines the bidding behavior of MEV bots using Flashbots' private channels, shedding light on the opaque dynamics of these auctions. We gather and analyze transaction data for the entire operational period of Flashbots, providing an extensive view of the current Ethereum MEV extraction landscape. Additionally, we engineer machine learning models that forecast winning bids whilst increasing profitability, capitalizing on our comprehensive transaction data analysis. Given our unique status as an adaptive entity, the findings reveal that our machine learning models can secure victory in more than 50% of Flashbots auctions, consequently yielding superior returns in comparison to current bidding strategies in arbitrage MEV auctions. Furthermore, the study highlights the relative advantages of adaptive constant bidding strategies in sandwich MEV auctions.

We provide a preliminary report of our ongoing work in formally defining and verifying, in a compositional way, the R1CS gadgets generated by Aleo's snarkVM. The approach is applicable to other systems that generate gadgets in a similar manner, and that may use non-R1CS representations.

As integrated circuit (IC) design and manufacturing have become highly globalized, hardware security risks become more prominent as malicious parties can exploit multiple stages of the supply chain for profit. Two potential targets in this chain are third-party intellectual property (3PIP) vendors and their customers. Untrusted parties can insert hardware Trojans into 3PIP circuit designs that can both alter device functionalities when triggered or create a side channel to leak sensitive information such as cryptographic keys. To mitigate this risk, the absence of Trojans in 3PIP designs should be verified before integration, imposing a major challenge for vendors who have to argue their IPs are safe to use, while also maintaining the privacy of their designs before ownership is transferred. To achieve this goal, in this work we employ modern cryptographic protocols for zero-knowledge proofs and enable 3PIP vendors prove an IP design is free of Trojan triggers without disclosing the corresponding netlist. Our approach uses a specialized circuit compiler that transforms arbitrary netlists into a zero-knowledge-friendly format, and introduces a versatile Trojan detection module that maintains the privacy of the actual netlist. We evaluate the effectiveness of our methodology using selected benchmarks.

Side-channel analysis certification is a process designed to certify the resilience of cryptographic hardware and software implementations against side-channel attacks. In certain cases, third-party evaluations by external companies or departments are necessary due to limited budget, time, or even expertise with the penalty of a significant exchange of sensitive information during the evaluation process. In this work, we investigate the potential of Homomorphic Encryption (HE) in performing side-channel analysis on HE-encrypted measurements. With HE applied to side-channel analysis (SCA), a third party can perform SCA on encrypted measurement data and provide the outcome of the analysis without gaining insights about the actual cryptographic implementation under test. To this end, we evaluate its feasibility by analyzing the impact of AI-based side-channel analysis using HE (private SCA) on accuracy and execution time and compare the results with an ordinary AI-based side-channel analysis (plain SCA). Our work suggests that both unprotected and protected cryptographic implementations can be successfully attacked already today with standard server equipment and modern HE protocols/libraries, while the traces are HE-encrypted.

Recently, the FinTracer algorithm was introduced as a versatile framework for detecting economic crime typologies in a privacy-preserving fashion. Under the hood, FinTracer stores its data in a structure known as the ``FinTracer tag’’. One limitation of FinTracer tags, however, is that because their underlying cryptographic implementation relies on additive semi-homomorphic encryption, all the system's oblivious computations on tag data are linear in their input ciphertexts. This allows a FinTracer user to combine information from multiple tags in some ways, but not generically. In this paper, we describe an efficient method to perform general nonlinear computations on FinTracer tags, and show how this ability can be used to detect a wide range of complex crime typologies, as well as to extract many new types of information, while retaining all of FinTracer's original privacy guarantees.

This paper initiates a new direction in the design and analysis of searchable symmetric encryption (SSE) schemes. We provide the first comprehensive security model and definition for SSE that takes into account leakage from the entirety of the SSE system, including not only from access to encrypted indices but also from access to the encrypted database documents themselves. Such system-wide leakage is intrinsic in end-to-end SSE systems, and can be used to break almost all state-of-the-art SSE schemes (Gui et al., IEEE S&P 2023). We then provide static and dynamic SSE constructions targeting our new notions. Our constructions involve a combination of novel techniques: bucketization to hide volumes of responses to queries; delayed, pseudorandom write-backs to disrupt access patterns; and indistinguishable search and update operations. The oblivious operations make it easy to establish strong versions of forward and backward security for our dynamic SSE scheme and rule out file-injection attacks. We implement our schemes and demonstrate that they offer very strong security against general classes of (system-wide) leakage-abuse attacks with moderate overhead. Our schemes scale smoothly to databases containing hundreds of thousand of documents and millions of keyword-document pairs. To the best of our knowledge, these are the first end-to-end SSE schemes that effectively suppress system-wide leakage while maintaining practical efficiency.

Privacy-preserving transaction systems on blockchain networks like Monero or Zcash provide complete transaction anonymity through cryptographic commitments or encryption. While this secures privacy, it inhibits the collection of statistical data, which current financial markets heavily rely on for economic and sociological research conducted by central banks, statistics bureaus, and research companies. Differential privacy techniques have been proposed to preserve individuals' privacy while still making aggregate analysis possible. We show that differential privacy and privacy-preserving transactions can coexist. We propose a modular scheme incorporating verifiable local differential privacy techniques into a privacy-preserving transaction system. We devise a novel technique that, on the one hand, ensures unbiased randomness and integrity when computing the differential privacy noise by the user and on the other hand, does not degrade the user's privacy guarantees.

Protecting metadata of communications has been an area of active research since the dining cryptographers problem was introduced by David Chaum in 1988. The Snowden revelations from 2013 resparked research in this direction. Consequently over the last decade we have witnessed a flurry of novel systems designed to protect metadata of users' communications online. However, such systems leverage different assumptions and design choices to achieve their goal; resulting in a scattered view of the desirable properties, potential vulnerabilities, and limitations of existing metadata-protecting communication systems (MPCS). In this work we survey 31 systems targeting metadata-protected communications, and present a unified view of the current state of affairs. We provide two different taxonomies for existing MPCS, first into four different categories by the precise type of metadata protections they offer, and next into six families based on the core techniques that underlie them. By contrasting these systems we identify potential vulnerabilities, as well as subtle privacy implications of design choices of existing MPCS. Furthermore, we identify promising avenues for future research for MPCS, and desirable properties that merit more attention.

CryptoNote blockchains like Monero represent the largest public deployments of linkable ring signatures. Beginning with the work of Kumar et al. (ESORICS 2017) and Möser et al. (PoPETs 2018), several techniques have been proposed to trace CryptoNote transactions, i.e. identify the actual signing key, by using the transaction history. Yu et al. (FC 2019) introduced the closed set attack for undeniable traceability and proved that it is optimal by showing that it has the same performance as the brute-force attack. However, they could only implement an approximation of the closed set attack due to its exponential time complexity. In this paper, we show that the Dulmage-Mendelsohn (DM) decomposition of bipartite graphs gives a polynomial-time implementation of the closed set attack. Our contribution includes open source implementations of the DM decomposition and the clustering algorithm (the approximation to the closed set attack proposed by Yu et al). Using these implementations, we evaluate the empirical performance of these methods on the Monero dataset in two ways -- firstly using data only from the main Monero chain and secondly using data from four hard forks of Monero in addition to the main Monero chain. We have released the scripts used to perform the empirical analysis along with step-by-step instructions.

Traditional finance quantifies risk by collecting and vetting reputation information for an individual, such as credit scores or payment history. While decentralized finance (DeFi) is an exceptionally well-suited application of permissionless blockchains, it is severely constrained in its ability to reconcile identities and quantify associated transaction risk directly on-chain. Opening the ecosystem to a broad range of use cases requires consistent pseudonymity and quantifiable reputation. This paper defies the status quo: exploring methods of assessing risk on-chain by efficiently integrating off-chain identity- and attribute- verification and on-chain transaction activity. We achieve this while preserving individual privacy within a competitive and fair environment and retaining compatibility with existing platforms such as Ethereum. Even though blockchains are inherently public, our solution gives users control over release of information that pertains to them. Consequently, our contribution focuses on customized methods that balance the degree of disclosure of provably-sourced user information against the likelihood of the user successfully gaining access to a desired resource, such as a loan under suitable terms. Our solution is consistent with the zero-trust model in that it imports explicit trust from recognized sources through relevant metrics that are subject to continuous update.

Decentralized Finance (DeFi), a blockchain-based financial ecosystem, suffers from smart contract vulnerabilities that led to a loss exceeding 3.24 billion USD by April 2022. To address this, blockchain firms audit DeFi applications, a process known as DeFi auditing. Our research aims to comprehend the mechanism and efficacy of DeFi auditing. We discovered its ability to detect vulnerabilities in smart contract logic and interactivity with other DeFi entities, but also noted its limitations in communication, transparency, remedial action implementation, and in preventing certain DeFi attacks. Moreover, our interview study delved into user perceptions of DeFi auditing, unmasking gaps in awareness, usage, and trust, and offering insights to address these issues.

System logs are crucial for forensic analysis, but to be useful, they need to be tamper-proof. To protect the logs, a number of secure logging systems have been proposed from both academia and the industry. Unfortunately, except for the recent KennyLoggings construction, all other logging systems are broken by an attack of Paccagnella et al. (CCS 2020). In this work, we build a secure logging system that improves KennyLoggings in several fronts: adoptability, security, and performance. Our key insight for performance gain is to use AES on a fixed, known key. While this trick is widely used in secure distributed computing, this is the first time it has found an application in the area of symmetric-key cryptography.

Real-world healthcare data sharing is instrumental in constructing broader-based and larger clinical data sets that may improve clinical decision-making research and outcomes. Stakeholders are frequently reluctant to share their data without guaranteed patient privacy, proper protection of their data sets, and control over the usage of their data. Fully homomorphic encryption (FHE) is a cryptographic capability that can address these issues by enabling computation on encrypted data without intermediate decryptions, so the analytics results are obtained without revealing the raw data. This work presents a toolset for collaborative privacy-preserving analysis of oncological data using multiparty FHE. Our toolset supports survival analysis, logistic regression training, and several common descriptive statistics. We demonstrate using oncological data sets that the toolset achieves high accuracy and practical performance, which scales well to larger data sets. As part of this work, we propose a novel cryptographic protocol for interactive bootstrapping in multiparty FHE, which is of independent interest. The toolset we develop is general-purpose and can be applied to other collaborative medical and healthcare application domains.

Protecting secret keys from malicious observers in untrusted environments is a critical security issue. White-box cryptography suggests software protection by hiding the key in the white-box setting. One method for hiding the key in the cipher code is through encoding methods. Unfortunately, encoding methods may be vulnerable to algebraic attacks and side-channel analysis. Another technique to hide the key is (M,Z)-space hardness approach that conceals the key into a large lookup table generated with a reliable small block cipher. In (M,Z)-space-hard algorithms, the key extraction problem in the white-box setting turns into a key recovery problem in the black-box setting. One of the problems for (M,Z)-space-hard algorithms is improving run-time performance. In this study, we aim to improve the run-time performance of the existing white-box implementations. We propose an LS-design based white-box algorithm with better run-rime performance than space-hard SPNbox algorithm. Moreover, an LS-design based table creation method is designed. When we compare the run-time performance of our method with the SPNbox algorithm, we obtain 28% improvement for white-box implementation and 27% for black-box implementation for 128-bit block size. The LS-design based method is also used for 256-bit block size in the white-box setting.

We present $\textit{Colordag}$, a blockchain protocol where following the prescribed strategy is, with high probability, a best response as long as all miners have less than $1/2$ of the mining power. We prove the correctness of Colordag even if there is an extremely powerful adversary who knows future actions of the scheduler: specifically, when agents will generate blocks and when messages will arrive. The state-of-the-art protocol, Fruitchain, is an $\varepsilon$-Nash equilibrium as long as all miners have less than $1/2$ of the mining power. However, there is a simple deviation that guarantees that deviators are never worse off than they would be by following Fruitchain, and can sometimes do better. Thus, agents are motivated to deviate. Colordag implements a solution concept that we call $\varepsilon\textit{-sure Nash equilibrium}$ and does not suffer from this problem. Because it is an $\varepsilon$-sure Nash equilibrium, Colordag is an $\varepsilon$-Nash equilibrium and with probability $1-\varepsilon$ is a best response.

Byzantine Fault-Tolerant (BFT) protocols that are based on Directed Acyclic Graphs (DAGs) are attractive due to their many advantages in asynchronous blockchain systems. These DAG-based protocols can be viewed as a simulation of some BFT protocol on a DAG. Many DAG-based BFT protocols rely on randomization, since they are used for agreement and ordering of transactions, which cannot be achieved deterministically in asynchronous systems. Randomization is achieved either through local sources of randomness, or by employing shared objects that provide a common source of randomness, e.g., common coins. A DAG simulation of a randomized protocol should be faithful, in the sense that it precisely preserves the properties of the original BFT protocol, and in particular, their probability distributions. We argue that faithfulness is ensured by a forward simulation. We show how to faithfully simulate any BFT protocol that uses public coins and shared objects, like common coins.

Nextcloud provides a server side encryption feature that is implemented by the Default Encryption Module. This paper presents cryptographic vulnerabilities that existed within the Default Encryption Module as well as other shortcomings that still need to be addressed. The vulnerabilities allowed an attacker to break the provided confidentiality and integrity protection guarantees. There is a high risk that ownCloud also contains some of the issues presented in this paper as it still has cryptographic code in common with Nextcloud.

There has been intense interest over the last decade in implementations of _probabilistic proofs_ (IPs, SNARKs, PCPs, and so on): protocols in which an untrusted party proves to a verifier that a given computation was executed properly, possibly in zero knowledge. Nevertheless, implementations still do not scale beyond small computations. A central source of overhead is the _front-end_: translating from the abstract computation to a set of equivalent arithmetic constraints. This paper introduces a general-purpose framework, called Distiller, in which a user translates to constraints not the original computation but an abstracted _specification_ of it. Distiller is the first in this area to perform such transformations in a way that is provably safe. Furthermore, by taking the idea of "encode a check in the constraints" to its literal logical extreme, Distiller exposes many new opportunities for constraint reduction, resulting in cost reductions for benchmark computations of 1.3–50$\times$, and in some cases, better asymptotics.

The open-source hardware IP model has recently started gaining popularity in the developer community. This model offers the integrated circuit (IC) developers wider standardization, faster time-to-market and richer platform for research. In addition, open-source hardware conforms to the Kerckhoff’s principle of a publicly-known algorithm and thus helps to enhance security. However, when security comes into consideration, source transparency is only one part of the solution. A complex global IC supply chain stands between the source and the final product. Hence, even if the source is known, the finished product is not guaranteed to match it. In this article, we propose the Open Scan model, in which, in addition to the source code, the IC vendor contributes a library-independent information on scan insertion. With scan information available, the user or a certification lab can perform partial reverse engineering of the IC to verify conformance to the advertised source. Compliance lists of open-source programs, such as of the OpenTitan cryptographic IC, can be amended to include this requirement. The Open Scan model addresses accidental and dishonest deviations from the golden model and partially addresses malicious modifications, known as hardware Trojans. We verify the efficiency of the proposed method in simulation with the Trust-Hub Trojan benchmarks and with several open-source benchmarks, in which we randomly insert modifications.

Many to many data sharing in the group setting in a cloud environment is a challenging problem that is crucial for numerous schemes. To our best knowledge, there is no generic study to allow sharing of confidential information in many to many pattern between different groups. Thus we propose a novel data sharing scheme enabling many to many sharing of encrypted data between different groups with using cryptographic techniques such as traceable ring signatures, multiple receiver key encapsulation, etc. We give a comprehensive security analysis showing our scheme is indistinguishable under user encapsulation keys and chosen plaintext attack secure under discrete logarithm assumption. We propose the implementation of our scheme, and the experimental results show that the proposed scheme is applicable for decentralized data sharing.

The applications of Hierarchical Deterministic Wallet are rapidly growing in various areas such as cryptocurrency exchanges and hardware wallets. Improving privacy and security is more important than ever. In this study, we proposed a protocol that fully support a two-party computation of BIP32. Our protocol, similar to the distributed key generation, can generate each party’s secret share, the common chain-code, and the public key without revealing a seed and any descendant private keys. We also provided a simulation-based proof of our protocol assuming a rushing, static, and malicious adversary in the hybrid model. Our master key generation protocol produces up to total of two bit leakages from a honest party given the feature that the seeds will be re-selected after each execution. The proposed hardened child key derivation protocol leads up to a one bit leakage in the worst situation of simulation from a honest party and will be accumulated with each execution. Fortunately, in reality, this issue can be largely mitigated by adding some validation criteria of boolean circuits and masking the input shares before each execution. We then implemented the proposed protocol and ran in a single thread on a laptop which turned out with practically acceptable execution time. Lastly, the outputs of our protocol can be easily integrated with many threshold sign protocols.

Mimblewimble is a cryptocurrency protocol with good privacy and scalability properties. A trade-off of Mimblewimble is the requirement that transactions are interactive between sender and receiver. TariScript is presented as an extension to Mimblewimble that adds scripting capabilities to the protocol. We describe the theoretical basis for TariScript and present the modifications required to make it secure. The trade-offs and use cases for TariScript are briefly covered.

Decentralized apps (DApps) often hold significant cryptocurrency assets. In order to manage these assets and coordinate joint investments, shareholders leverage the underlying smart contract functionality to realize a transparent, verifiable, and secure decision-making process. That is, DApps implement proposal-based voting. Permissionless blockchains, however, lead to a conflict between transparency and anonymity; potentially preventing free decision-making if individual votes and intermediate results become public. In this paper, we therefore present Tornado Vote, a voting DApp for anonymous, fair, and practical voting on the Ethereum blockchain. We propose to use a cryptocurrency mixer such as Tornado Cash to reconcile transparency and anonymity. To this end, we adapt Tornado Cash and develop a voting protocol that implements a fair voting process. While Tornado Vote can technically process 10k votes on Ethereum in approximately two hours, this is not feasible under realistic conditions: Third-party transactions on the Ethereum Mainnet reduce the possible throughput, and transaction fees make it infeasible to use all available block capacities. We therefore present various Gas cost models that yield lower bounds and economic estimations with respect to the required number of blocks and voting costs to assess and adjust Tornado Vote's feasibility trade-off.

We present TVA, a multi-party computation (MPC) system for secure analytics on secret-shared time series data. TVA achieves strong security guarantees in the semi-honest and malicious settings, and high expressivity by enabling complex analytics on inputs with unordered and irregular timestamps. TVA is the first system to support arbitrary composition of oblivious window operators, keyed aggregations, and multiple filter predicates, while keeping all data attributes private, including record timestamps and user-defined values in query predicates. At the core of the TVA system lie novel protocols for secure window assignment: (i) a tumbling window protocol that groups records into fixed-length time buckets and (ii) two session window protocols that identify periods of activity followed by periods of inactivity. We also contribute a new protocol for secure division with a public divisor, which may be of independent interest. We evaluate TVA on real LAN and WAN environments and show that it can efficiently compute complex window-based analytics on inputs of $2^{22}$ records with modest use of resources. When compared to the state-of-the-art, TVA achieves up to $5.8\times$ lower latency in queries with multiple filters and two orders of magnitude better performance in window aggregation.

Frequently, users on the web need to show that they are, for example, not a robot, old enough to access an age restricted video, or eligible to download an ebook from their local public library without being tracked. Anonymous credentials were developed to address these concerns. However, existing schemes do not handle the realities of deployment or the complexities of real-world identity. Instead, they implicitly make assumptions such as there being an issuing authority for anonymous credentials that, for real applications, requires the local department of motor vehicles to issue sophisticated cryptographic tokens to show users are over 18. In reality, there are multiple trust sources for a given identity attribute, their credentials have distinctively different formats, and many, if not all, issuers are unwilling to adopt new protocols. We present and build zk-creds, a protocol that uses general-purpose zero-knowledge proofs to 1) remove the need for credential issuers to hold signing keys: credentials can be issued to a bulletin board instantiated as a transparency log, Byzantine system, or even a blockchain; 2) convert existing identity documents into anonymous credentials without modifying documents or coordinating with their issuing authority; 3) allow for flexible, composable, and complex identity statements over multiple credentials. Concretely, identity assertions using zk-creds take less than 150ms in a real-world scenario of using a passport to anonymously access age-restricted videos.

Fully homomorphic encryption suffers from a large expansion in the size of encrypted data, which makes FHE impractical for low-bandwidth networks. Fortunately, transciphering allows to circumvent this issue by involving a symmetric cryptosystem which does not carry the disadvantage of a large expansion factor, and maintains the ability to recover an FHE ciphertext with the cost of extra homomorphic computations on the receiver side. Recent works have started to investigate the efficiency of TFHE as the FHE layer in transciphering, combined with various symmetric schemes including a NIST finalist for lightweight cryptography, namely Grain128-AEAD. Yet, this has so far been done without taking advantage of TFHE functional bootstrapping abilities, that is, evaluating any discrete function ``for free'' within the bootstrapping operation. In this work, we thus investigate the use of TFHE functional bootstrapping for implementing Grain128-AEAD in a more efficient base ($B > 2$) representation, rather than a binary one. This significantly reduces the overall number of necessary bootstrappings in a homomorphic run of the stream-cipher, for example reducing the number of bootstrappings required in the warm-up phase by a factor of $\approx$ 3 when $B=16$.

The threat of chip-level tampering and its detection has been widely researched. Hardware Trojan insertions are prominent examples of such tamper events. Altering the placement and routing of a design or removing a part of a circuit for side-channel leakage/fault sensitivity amplification are other instances of such attacks. While semi- and fully-invasive physical verification methods can confidently detect such stealthy tamper events, they are costly, time-consuming, and destructive. On the other hand, virtually all proposed non-invasive side-channel methods suffer from noise and, therefore, have low confidence. Moreover, they require activating the tampered part of the circuit (e.g., the Trojan trigger) to compare and detect the modifications. In this work, we introduce a non-invasive post-silicon tamper detection technique applicable to different classes of tamper events at the chip level without requiring the activation of the malicious circuit. Our method relies on the fact that physical modifications (regardless of their physical, activation, or action characteristics) alter the impedance of the chip. Hence, characterizing the impedance can lead to the detection of the tamper events. To sense the changes in the impedance, we deploy known RF tools, namely, scattering parameters, in which we inject sine wave signals with high frequencies to the power distribution network (PDN) of the system and measure the “echo” of the signal. The reflected signals in various frequency bands reveal different tamper events based on their impact size on the die. To validate our claims, we performed measurements on several proof-of-concept tampered hardware implementations realized on FPGAs manufactured with a 28 nm technology. We further show that deploying the Dynamic Time Warping (DTW) distance can distinguish between tamper events and noise resulting from manufacturing process variation of different chips/boards. Based on the acquired results, we demonstrate that stealthy hardware Trojans, as well as sophisticated modifications of P&R, can be detected.

In this work, we study the blockchain leader election problem. The purpose of such protocols is to elect a leader who decides on the next block to be appended to the blockchain, for each block proposal round. Solutions to this problem are vital for the security of blockchain systems. We introduce an efficient blockchain leader election method with security based solely on standard assumptions for cryptographic hash functions (rather than public-key cryptographic assumptions) and that does not involve a racing condition as in Proof-of-Work based approaches. Thanks to the former feature, our solution provides the highest confidence in security, even in the post-quantum era. A particularly scalable application of our solution is in the Proof-of-Stake setting, and we investigate our solution in the Algorand blockchain system. We believe our leader election approach can be easily adapted to a range of other blockchain settings. At the core of Algorand's leader election is a verifiable random function (VRF). Our approach is based on introducing a simpler primitive which still suffices for the blockchain leader election problem. In particular, we analyze the concrete requirements in an Algorand-like blockchain setting to accomplish leader election, which leads to the introduction of indexed VRF (iVRF). An iVRF satisfies modified uniqueness and pseudorandomness properties (versus a full-fledged VRF) that enable an efficient instantiation based on a hash function without requiring any complicated zero-knowledge proofs of correct PRF evaluation. We further extend iVRF to an authenticated iVRF with forward-security, which meets all the requirements to establish an Algorand-like consensus. Our solution is simple, flexible and incurs only a 32-byte additional overhead when combined with the current best solution to constructing a forward-secure signature (in the post-quantum setting). We implemented our (authenticated) iVRF proposal in C language on a standard computer and show that it significantly outperforms other quantum-safe VRF proposals in almost all metrics. Particularly, iVRF evaluation and verification can be executed in 0.02 ms, which is even faster than ECVRF used in Algorand.

Efficiently and securely removing encrypted redundant data with cross-user in the cloud is challenging. Convergent Encryption (CE) is difficult to resist dictionary attacks for its deterministic tag. Server-aided mechanism is against such attacks while it may exist collusion. Focus on multimedia data, this paper proposes an efficient and secure fuzzy deduplication system without any additional servers. We also propose a notion of preverification of label consistency to compensate for the irreparable post-verification loss. Compared with other fuzzy deduplication schemes, our work has apparent advantages in deduplication efficiency and security based on a natural data set.

Applying the pothole method on the factors of numbers of the form $2^n-1$, we prove the stronger inequality $$\iota(2^n-1)\leq n+1-\sum \limits_{j=1}^{\lfloor \frac{\log n}{\log 2}\rfloor}\xi(n,j)+3\lfloor\frac{\log n}{\log 2}\rfloor$$ for all $n\in \mathbb{N}$ with $n\geq 64$ for $0\leq \xi(n,j)<1$, where $\iota(\cdot)$ denotes the length of the shortest addition chain producing $\cdot$. This inequality is stronger than $$\iota(r)<\frac{\log r}{\log 2}(1+\frac{1}{\log \log r}+\frac{2\log 2}{(\log r)^{1-\log 2}})$$ in the case $r=2^n-1$ but slightly weaker than the conjectured inequality $$\iota(2^n-1)\leq n-1+\iota(n).$$

Physical attacks, including passive Side-Channel Analysis and active Fault Injection Analysis, are considered among the most powerful threats against physical cryptographic implementations. These attacks are well known and research provides many specialized countermeasures to protect cryptographic implementations against them. Still, only a limited number of combined countermeasures, i.e., countermeasures that protect implementations against multiple attacks simultaneously, were proposed in the past. Due to increasing complexity and reciprocal effects, design of efficient and reliable combined countermeasures requires longstanding expertise in hardware design and security. With the help of formal security specifications and adversary models, automated verification can streamline development cycles, increase quality, and facilitate development of robust cryptographic implementations. In this work, we revise and refine formal security notions for combined protection mechanisms and specifically embed them in the context of hardware implementations. Based on this, we present the first automated verification framework that can verify physical security properties of hardware circuits with respect to combined physical attacks. To this end, we conduct several case studies to demonstrate the capabilities and advantages of our framework, analyzing secure building blocks (gadgets), S-boxes build from Toffoli gates, and the ParTI scheme. For the first time, we reveal security flaws in analyzed structures due to reciprocal effects, highlighting the importance of continuously integrating security verification into modern design and development cycles.

Cryptocurrencies are used in several, distinct use cases, thereby sustaining the existence of many ledgers that are heterogeneous in terms of design and purpose. In addition, the interest of central banks in deploying Central Bank Digital Currency (CBDC) has spurred a blooming number of conceptually different proposals from central banks and academia. As a result of the diversity of cryptocurrency and CBDC ledgers, interoperability, i.e., the seamless transfer of value between users that operate on different ledgers, has become an interesting research problem. In fact, interoperability has been explored both in CBDC and cryptocurrencies, and numerous proposals exist. However, these proposals are tailored to the characteristics of the ledgers for which they are designed. For instance, some rely on trusted hardware, others rely on the scripting capabilities of the underlying ledger or on specific cryptographic assumptions of the transaction authorization mechanism (e.g., adaptor signatures), while others rely on a trusted entity. This fragmentation results in the repetitive development of interoperablity protocols that address the same applications across the various ledgers. In this work, we propose an alternative approach: decouple the transaction authorization details of each ledger from the definition of cross-ledger applications. To do so, we define a middle layer that abstracts the core functionality for authorizing transactions in any ledger and the security notions of interest. This middle layer serves two purposes: (i) it becomes the main cryptographic building block to (re)define cross-ledger applications in a ledger agnostic manner; (ii) for any ledger that exists (and the new to come), it suffices to prove that it is a secure instance of the middle layer to be compatible with cross-ledger protocols. We define two new primitives for our middle layer, the basic payment ledger (BL) and the conditional payment ledger (CL). We prove that the two most common transaction authorization mechanism (digital signatures and zero knowledge proofs) are secure constructions for BL. We also prove that common smart contracts (e.g., HTLC and PTLC) and the combination of adaptor signatures with verifiable timelock puzzles are also secure constructions for CL. Finally, we discuss how to design some popular applications (e.g. atomic swaps between ledgers) using our middle layer.

Zero-knowledge proof systems are becoming increasingly prevalent and being widely used to secure decentralized financial systems and protect the privacy of users. Given the sensitivity of these applications, zero-knowledge proof systems are a natural target for formal verification methods. We describe methods for checking one such proof system: Halo2. We use abstract interpretation and an SMT solver to check various properties of Halo2 circuits. Using abstract interpretation, we can detect unused gates, unconstrained cells, and unused columns. Using an SMT solver, we can detect under-constrained (in the sense that for the same public input they have two efficiently computable satisfying assignments) circuits. This is the first work we are aware of that applies lightweight formal methods to PLONKish arithmetization and Halo2 circuits.

Privacy-preserving machine learning (PPML) promises to train machine learning (ML) models by combining data spread across multiple data silos. Theoretically, secure multiparty computation (MPC) allows multiple data owners to train models on their joint data without revealing the data to each other. However, the prior implementations of this secure training using MPC have three limitations: they have only been evaluated on CNNs, and LSTMs have been ignored; fixed point approximations have affected training accuracies compared to training in floating point; and due to significant latency overheads of secure training via MPC, its relevance for practical tasks with streaming data remains unclear. The motivation of this work is to report our experience of addressing the practical problem of secure training and inference of models for urban sensing problems, e.g., traffic congestion estimation, or air pollution monitoring in large cities, where data can be contributed by rival fleet companies while balancing the privacy-accuracy trade-offs using MPC-based techniques. Our first contribution is to design a custom ML model for this task that can be efficiently trained with MPC within a desirable latency. In particular, we design a GCN-LSTM and securely train it on time-series sensor data for accurate forecasting, within 7 minutes per epoch. As our second contribution, we build an end-toend system of private training and inference that provably matches the training accuracy of cleartext ML training. This work is the first to securely train a model with LSTM cells. Third, this trained model is kept secret-shared between the fleet companies and allows clients to make sensitive queries to this model while carefully handling potentially invalid queries. Our custom protocols allow clients to query predictions from privately trained models in milliseconds, all the while maintaining accuracy and cryptographic security

Vehicular Social Networks (VSNs) rely on data shared by users to provide convenient services. Data is outsourced to the cloud server and the distributed roadside unit in VSNs. However, roadside unit has limited resources, so that data sharing process is inefficient and is vulnerable to security threats, such as illegal access, tampering attack and collusion attack. In this article, to overcome the shortcomings of security, we define a chain tolerance semi-trusted model to describe the credibility of distributed group based on the anti tampering feature of blockchain. We further propose a Blockchain-based Lightweight Access Control scheme in VSNs that resist tampering and collusion attacks, called BLAC. To overcome the shortcomings of efficiency, we design a ciphertext piece storage algorithm and a recovery one to achieve lightweight storage cost. In the decryption operation, we separate a pre-decryption algorithm based on outsourcing to achieve lightweight decryption computation cost on the user side. Finally, we present the formal security analyses and the simulation experiments for BLAC, and compare the results of experiments with existing relevant schemes. The security analyses show that our scheme is secure, and the results of experiments show that our scheme is lightweight and practical.

In this paper, we present a novel method for timestamping and data notarisation on a distributed ledger. The problem with on-chain hashes is that a cryptographic hash is a deterministic function that it allows the blockchain be used as an oracle that confirms whether potentially leaked data is authentic (timestamped or notarised by the user). Instead, we suggest using on-chain Pedersen commitments and off-chain zero knowledge proofs (ZKP) for designated verifiers to prove the link between the data and the on-chain commitment. Our technique maintains the privacy of the data, and retains control of who can access it and when they can access it. This holds true even on a public blockchain, and even if the data is leaked by authorised parties. Indeed, an authorised data consumer (a designated-verifier for the ZKP), who discloses the data publicly, cannot convince anyone about the legitimacy of the data (in the sense that it is consistent with the information uploaded to the blockchain), because the ZKP proof is valid only for them. Our techniques can be used in scenarios where it is required to audit highly-sensitive data (e.g. application logs) by specific third parties, or to provide on-demand data certification by notaries

With the advancement in technology, Cloud computing always amazes the world with revolutionizing solutions that automate and simplify complex computational tasks. The advantages like no maintenance cost, accessibility, data backup, pay-per-use models, unlimited storage, and processing power encourage individuals and businesses to migrate their workload to the cloud. Despite the numerous advantages of cloud computing, the geolocation of data in the cloud environment is a massive concern, which relates to the performance and government legislation that will be applied to data. The unclarity of data geolocation can cause compliance concerns. In this work, we have presented a technique that will allow users to restrict the geolocation of their data in the cloud environment. We have used trusted computing mechanisms to attest the host and its geolocation remotely. With this model, the user will upload the data whose decryption key will be shared with a third-party attestation server only. The decryption key will be sealed to the TPM of the host after successful attestation guaranteeing the authorized geolocation and platform state.

A possible new approach to the Zodiac Killer's 32-Character Cipher (Z32) is proposed based on the strengths and weaknesses of previous approaches and novel interpretations. This approach does not assume the use of anagrams or similar complex transposition methods; does not assume the identity of a particular Zodiac suspect; and assumes the use of homophonic substitution (as in Z408 and Z340), and simple transposition (as in Z340). Assumptions are clearly defined and tested with sensitivity tests. With Mount Diablo as the pole of a plane polar coordinate system, the instruction "set to Mag. N." is interpreted by setting the hour and minute hands of a watchface to the magnetic declination of the Bay Area circa 1970. Sensitivity tests reveal the exact year and location have little impact on the declination in this case. The hour and minute given by the hands are interpreted as the radial coordinate r and the angular coordinate theta, as in "Radians & # inches along the radians". The hand corresponding to each coordinate is tested, as are 12- and 24-hour interpretations. Impossible or improbable coordinates are excluded leaving one coordinate as a possible solution. This coordinate is explored as the possible plaintext for Z32 using the Z340 transposition method. Further exploration of the proposed method is necessary.

Timed commitments (Boneh and Naor, CRYPTO 2000) are a variant of standard commitments which incorporates a forced opening mechanism that allows anyone to reveal the committed message, but not before a certain prescribed date. Timed commitments have a wide-range of applications such as contract signing, fair multi-party computation, sealed bid auctions or new blockchain applications such as preventing front-running or unbiased randomness generation. We revisit the notion of timed commitments and propose an alternative simplified definition. We also provide two new constructions of timed commitments with different trade-offs.

Society appears to be on the verge of recognizing the need for control over sensitive data in modern web applications. Recently, many systems claim to give control to individuals, promising the preeminent goal of data sovereignty. However, despite recent attention, research and industry efforts are fragmented and lack a holistic system overview. In this paper, we provide the first transecting systematization of data sovereignty by drawing from a dispersed body of knowledge. We clarify the field by identifying its three main areas: (i) decentralized identity, (ii) decentralized access control and (iii) policy-compliant decentralized computation. We find that literature lacks a cohesive set of formal definitions. Each area is considered in isolation, and priorities in industry and academia are not aligned due to a lack of clarity regarding user control. To solve this issue, we propose formal definitions for each sub-area. By highlighting that data sovereignty transcends the domain of decentralized identity, we aim to guide future works to embrace a broader perspective on user control. In each section, we augment our definition with security and privacy properties, discuss the state of the art and proceed to identify open challenges. We conclude by highlighting synergies between areas, emphasizing the real-world benefit obtained by further developing data sovereign systems.

With the announcement of the first winners of the NIST Post-Quantum Cryptography (PQC) competition in 2022, the industry has now a confirmed foundation to revisit established cryptographic algorithms applied in automotive use cases and replace them with quantum-safe alternatives. In this paper, we investigate the application of the NIST competition winner CRYSTALS-Dilithium to protect the integrity and authenticity of over-the-air update packages. We show how this post-quantum secure digital signature algorithm can be integrated in AUTOSAR Adaptive Platform Update and Configuration Management framework and evaluate our approach practically using the NXP S32G vehicle network processor. We discuss two implementation variants with respect to performance and resilience against relevant attacks, and conclude that PQC has little impact on the update process as a whole.

Integrating traditional Internet (web2) identities with blockchain (web3) identities presents considerable obstacles. Conventional solutions typically employ a mapping strategy, linking web2 identities directly to specific blockchain addresses. However, this method can lead to complications such as fragmentation of identifiers across disparate networks. To address these challenges, we propose a novel scheme, Address Abstraction (AA), that circumvents the need for direct mapping. AA scheme replaces the existing blockchain address system while maintaining essential properties including a unique identifier, immutability of requests, and privacy preservation. This capability allows users to interact with the blockchain via their web2 identities, irrespective of the specific blockchain address, thereby eliminating limitations tied to a blockchain-specific address system. This mechanism fosters the seamless integration of web2 identities within the web3, in addition, promotes cross-chain compatibility. We also provide an application of AA, denoted as zero-knowledge Address Abstraction (zkAA). It mainly leverages the zero-knowledge proofs to ensure the properties of AA. zkAA has been implemented as a smart contract — compatible with any existing contract-enabled blockchains. Our evaluation of zkAA on Ethereum demonstrates its efficiency. The average cost for registering an abstracted identity is approximate \$7.66, whereas publishing an abstracted transaction costs around \$4.75. In contrast, on Polygon, the associated costs are markedly lower: \$0.02 for registration and \$0.01 for publication, as of January 13, 2023. This empirical evaluation substantiates the feasibility of our proposed solution.

Blockchain systems can become overwhelmed by a large number of transactions, leading to increased latency. As a consequence, latency-sensitive users must bid against each other and pay higher fees to ensure that their transactions are processed in priority. However, most of the time of a blockchain system (78% in Ethereum), there is still a lot of unused computational power, with few users sending transactions. To address this issue and reduce latency for users, we propose the latency-first smart contract model in this paper, which optimistically accepts committed transactions. This allows users to submit a commitment during times of high demand, and then complete the rest of the work at a lower priority. From the perspective of the blockchain, this temporarily “overclocks” the system. We have developed a programming tool for our model, and our experiments show that the proposed latency-first smart contract model can greatly reduce latency during the periods of high demand.

Recent years have seen the emergence of practical advanced cryptographic tools that not only protect data privacy and authenticity, but also allow for jointly processing data from different institutions without sacrificing privacy. The ability to do so has enabled implementations a number of traditional and decentralized financial applications that would have required sacrificing privacy or trusting a third party. The main catalyst of this revolution was the advent of decentralized cryptocurrencies that use public ledgers to register financial transactions, which must be verifiable by any third party, while keeping sensitive data private. Zero Knowledge (ZK) proofs rose to prominence as a solution to this challenge, allowing for the owner of sensitive data (e.g. the identities of users involved in an operation) to convince a third party verifier that a certain operation has been correctly executed without revealing said data. It quickly became clear that performing arbitrary computation on private data from multiple sources by means of secure Multiparty Computation (MPC) and related techniques allows for more powerful financial applications, also in traditional finance. In this SoK, we categorize the main traditional and decentralized financial applications that can benefit from state-of-the-art Privacy-Enhancing Technologies (PETs) and identify design patterns commonly used when applying PETs in the context of these applications. In particular, we consider the following classes of applications: 1. Identity Management, KYC & AML; and 2. Markets & Settlement; 3. Legal; and 4. Digital Asset Custody. We examine how ZK proofs, MPC and related PETs have been used to tackle the main security challenges in each of these applications. Moreover, we provide an assessment of the technological readiness of each PET in the context of different financial applications according to the availability of: theoretical feasibility results, preliminary benchmarks (in scientific papers) or benchmarks achieving real-world performance (in commercially deployed solutions). Finally, we propose future applications of PETs as Fintech solutions to currently unsolved issues. While we systematize financial applications of PETs at large, we focus mainly on those applications that require privacy preserving computation on data from multiple parties.

In this work, we revisit the severely limited throughput problem of cryptocurrencies and propose a novel rebalancing approach for Payment Channel Networks (PCNs). PCNs are a popular solution for increasing the blockchain throughput, however, their benefit depends on the overall users’ liquidity. Rebalancing mechanisms are the state-of-the-art approach to maintaining high liquidity in PCNs. However, existing opt-in rebalancing mechanisms exclude users that may assist in rebalancing for small service fees, leading to suboptimal solutions and under-utilization of the PCNs’ bounded liquidity. We introduce the first rebalancing approach for PCNs that includes all users, following an “all for one and one for all” design philosophy that yields optimal throughput. The proposed approach introduces a double-auction rebalancing problem, which we term Musketeer, where users can participate as buyers (paying fees to rebalance) or sellers (charging fees to route transactions). The desired properties tailored to the unique characteristics of PCNs are formally defined, including the novel property of cyclic budget balance that is a stronger variation of strong budget balance. Basic results derived from auction theory, including an impossibility and multiple mechanisms that either achieve all desiderata under a relaxed model or sacrifice one of the properties, are presented. We also propose a novel mechanism that leverages time delays as an additional cost to users. This mechanism is provably truthful, cyclic budget balanced, individually rational, and economic efficient but only with respect to liquidity.

TLS termination, which is essential to network and security infrastructure providers, is an extremely latency sensitive operation that benefits from access to sensitive key material close to the edge. However, increasing regulatory concerns prompt customers to demand sophisticated controls on where their keys may be accessed. While traditional access-control solutions rely on a highly available centralized process to enforce access, the round-trip latency and decreased fault tolerance make this approach unappealing. Furthermore, the desired level of customer control is at odds with customizing the distribution process for each key. To solve this dilemma, we have designed and implemented Portunus, a cryptographic storage and access control system built using a variant of public-key cryptography called attribute-based encryption (ABE). Using Portunus, TLS keys are protected using ABE under a policy chosen by the customer. Each server is issued unique ABE keys based on its attributes, allowing it to decrypt only the TLS keys for which it satisfies the policy. Thus, the encrypted keys can be stored at the edge, with access control enforced passively through ABE. If a server receives a TLS connection but is not authorized to decrypt the necessary TLS key, the request is forwarded directly to the nearest authorized server, further avoiding the need for a centralized coordinator. In comparison, a trivial instantiation of this system using standard public-key cryptography might wrap each TLS key with the key of every authorized data center. This strategy, however, multiplies the storage overhead by the number of data centers. We have deployed Portunus on Cloudflare's global network of over 400 data centers. Our measurements indicate that we can handle millions of requests per second globally, making it one of the largest deployments of ABE.

In this work, we propose the notion of homomorphic indistinguishability obfuscation ($\mathsf{HiO}$) and present a construction based on subexponentially-secure $\mathsf{iO}$ and one-way functions. An $\mathsf{HiO}$ scheme allows us to convert an obfuscation of circuit $C$ to an obfuscation of $C'\circ C$, and this can be performed obliviously (that is, without knowing the circuit $C$). A naive solution would be to obfuscate $C' \circ \mathsf{iO}(C)$. However, if we do this for $k$ hops, then the size of the final obfuscation is exponential in $k$. $\mathsf{HiO}$ ensures that the size of the final obfuscation remains polynomial after repeated compositions. As an application, we show how to build function-hiding hierarchical multi-input functional encryption and homomorphic witness encryption using $\mathsf{HiO}$.

Payment channels allow a sender to do multiple transactions with a receiver without recording each single transaction on-chain. While most of the current constructions for payment channels focus on UTXO-based cryptocurrencies with reduced scripting capabilities (e.g., Bitcoin or Monero), little attention has been given to the possible benefits of adapting such constructions to cryptocurrencies based on the account model and offering a Turing complete language (e.g., Ethereum). The focus of this work is to implement efficient payment channels tailored to the capabilities of account-based cryptocurrencies with Turing-complete language support in order to provide scalable payments that are interoperable across different cryptocurrencies and unlinkable for third-parties (e.g., payment intermediaries). More concretely, we continue the line of research on cryptocurrency universal payment channels (UPC) which facilitate interoperable payment channel transactions across different ledgers in a hub-and-spoke model, by offering greater scalability than point-to-point architectures. Our design proposes two different versions, UPC and AUPC. For UPC we formally describe the protocol ideas sketched in previous work and evaluate our proof-of-concept implementation. Then, AUPC further extends the concept of universal payment channels by payment unlinkability against the intermediary server.

Frontrunning is a major problem in DeFi applications, such as blockchain-based exchanges. Albeit, existing solutions are not practical and/or they make external trust assumptions. In this work we propose a market-maker-based crypto-token exchange, which is both more efficient than existing solutions and offers provable resistance to frontrunning attack. Our approach combines in a clever way a game theoretic analysis of market-makers with new cryptography and blockchain tools to defend against all three ways by which an exchange might front-run, i.e., (1) reorder trade requests, (2) adaptively drop trade requests, and (3) adaptively insert (its own) trade requests. Concretely, we propose novel light- weight cryptographic tools and smart-contract-enforced incentives to eliminate reordering attacks and ensure that dropping requests have to be oblivious (uninformed) of the actual trade. We then prove that with these attacks eliminated, a so-called monopolistic market-maker has no longer incentives to add or drop trades. We have implemented and benchmarked our exchange and provide concrete evidence of its advantages over existing solutions.

In most verifiable electronic voting schemes, one key step is the tally phase, where the election result is computed from the encrypted ballots. A generic technique consists in first applying (verifiable) mixnets to the ballots and then revealing all the votes in the clear. This however discloses much more information than the result of the election itself (that is, the winners) and may offer the possibility to coerce voters. In this paper, we present a collection of building blocks for designing tally-hiding schemes based on multi-party computations. As an application, we propose the first efficient tally-hiding schemes with no leakage for four important counting functions: D'Hondt, Condorcet, STV, and Majority Judgment. We prove that they can be used to design a private and verifiable voting scheme. We also unveil unknown flaws or leakage in several previously proposed tally-hiding schemes.

Imagine that a privacy-conscious client would like to query a key-value store residing on an untrusted server equipped with a secure processor. To protect the privacy of the client's queries as well as the database, one approach is to implement an {\it oblivious map} inside a secure enclave. Indeed, earlier works demonstrated numerous applications of an enclaved-based oblivious map, including private contact discovery, key transparency, and secure outsourced databases. Our work is motivated by the observation that the previous enclave implementations of oblivious algorithms are sub-optimal both asymptotically and concretely. We make the key observation that for enclave applications, the {\it number of page swaps} should be a primary performance metric. We therefore adopt techniques from the {\it external-memory} algorithms literature, and we are the first to implement such algorithms inside hardware enclaves. We also devise asymptotically better algorithms for ensuring a strong notion of obliviousness that resists cache-timing attacks. We complement our algorithmic improvements with various concrete optimizations that save constant factors in practice. The resulting system, called Enigmap, achieves 15$\times$ speedup over Signal's linear scan implementation, and 53$\times$ speedup over the %state-of-the-art prior best oblivious algorithm implementation, at a realistic database size of 256 million and a batch size of 1000. The speedup is asymptotical in nature and will be even greater as Signal's user base grows.

Digital and paper-based authentication are the two predominant mechanisms that have been deployed in the real world to authenticate end-users. When verification of a digital credential is performed in person (e.g. the authentication that was often required to access facilities at the peak of the COVID global pandemic), the two mechanisms are often deployed together: the verifier checks government-issued ID to match the picture on the ID to the individual holding it, and then checks the digital credential to see that the personal details on it match those on the ID, and to discover additional attributes of the holder. This pattern is extremely common and very likely to remain in place for the foreseeable future. However, it poses an interesting problem: if the digital credential is privacy-preserving (e.g. based on BBS+ on CL signatures), but the holder is still forced to show an ID card or a passport to verify that the presented credential was indeed issued to the holder, what is the point of deploying privacy-preserving digital credential? In this paper we address this problem by redefining what an ID card should show, and force a minimal but mandatory involvement of the card in the digital interaction. Our approach permits verifiers to successfully authenticate holders and to determine that they are the rightful owners of the digital credential. At the same time, optimal privacy guarantees are preserved. We design our scheme, formally define and analyse its security in the Universal Composability (UC) framework, and implement the card component, showing the running time to be below 200ms irrespective of the number of certified attributes.

Homomorphic Encryption (HE) is a type of cryptography that allows computing on encrypted data, enabling computation on sensitive data to be outsourced securely. Many popular HE schemes rely on noise for their security. On the other hand, Differential Privacy seeks to guarantee the privacy of data subjects by obscuring any one individual's contribution to an output. Many mechanisms for achieving Differential Privacy involve adding appropriate noise. In this work, we investigate the extent to which the noise native to Homomorphic Encryption can provide Differential Privacy "for free". We identify the dependence of HE noise on the underlying data as a critical barrier to privacy, and derive new results on the Differential Privacy under this constraint. We apply these ideas to a proof of concept HE application, ridge regression training using gradient descent, and are able to achieve privacy budgets of $\varepsilon \approx 2$ after 50 iterations.

The ecosystem around blockchain and Decentralized Finance (DeFi) is seeing more and more interest from centralized regulators. For instance, recently, the US government placed sanctions on the largest DeFi mixer, Tornado.Cash (TC). To our knowledge, this is the first time that centralized regulators sanction a decentralized and open-source blockchain application. It has led various blockchain participants, e.g., miners/validators and DeFi platforms, to censor TC-related transactions. The blockchain community has extensively discussed that censoring transactions could affect users’ privacy. In this work, we analyze the efficiency and possible security implications of censorship on the different steps during the life cycle of a blockchain transaction, i.e., generation, propagation, and validation. We reveal that fine-grained censorship will reduce the security of block validators and centralized transaction propagation services, and can potentially cause Denial of Service (DoS) attacks. We also find that DeFi platforms adopt centralized third-party services to censor user addresses at the frontend level, which blockchain users could easily bypass. Moreover, we present a tainting attack whereby an adversary can prevent users from interacting normally with DeFi platforms by sending TC-related transactions.

The Lightning Network provides almost-instant payments to its parties. In addition to direct payments requiring a shared payment channel, parties can pay each other in the form of multi-hop payments via existing channels. Such multi-hop payments rely on a 2-phase commit protocol to achieve balance security; that is, no honest intermediary party loses her coins. Unfortunately, failures or attacks in this 2-phase commit protocol can lead to coins being committed (locked) in a payment for extended periods of time (in the order of days in the worst case). During these periods, parties cannot go offline without losing funds due to their existing commitments, even if they use watchtowers. Furthermore, they cannot use the locked funds for initiating or forwarding new payments, reducing their opportunities to use their coins and earn fees. We introduce Bailout, the first protocol that allows intermediary parties in a multi-hop payment to unlock their coins before the payment completes by re-routing the payment over an alternative path. We achieve this by creating a circular payment route starting from the intermediary party in the opposite direction of the original payment. Once the circular payment is locked, both payments are canceled for the intermediary party, which frees the coins of the corresponding channels. This way, we create an alternative route for the ongoing multi-hop payment without involving the sender or receiver. The parties on the alternative path are incentivized to participate through fees. We evaluate the utility of our protocol using a real-world Lightning Network snapshot. Bailouts may fail due to insufficient balance in alternative paths used for re-routing. We find that attempts of a node to bailout typically succeed with a probability of more than 94% if at least one alternative path exists.

Coercion-resistance is one of the most challenging security properties to achieve when designing an e-voting protocol. The JCJ voting scheme, proposed in 2005 by Juels, Catalano and Jakobsson, is one of the first voting systems where coercion-resistance was rigorously defined and achieved, making JCJ the benchmark for coercion-resistant protocols. Recently, the coercion-resistance definition proposed in JCJ has been disputed and improved by Cortier, Gaudry, and Yang. They identified a major problem, related to leakage of the number of discarded votes by revoting; and proposed CHide, a new protocol that solves the issue and satisfies a stronger security notion. In this work we present an improved version of CHide, with complexity $O(n\log n)$ instead of $O(n^2)$ in the number $n$ of received ballots, that relies on sorting encrypted ballots to make the tallying phase faster. The asymptotic complexity of our protocol is competitive with other state-of-the-art coercion-resistant voting protocols satisfying the stronger notion for coercion resistance.

Trusted Execution Environments (TEEs) suffer from performance issues when executing certain management instructions, such as creating an enclave, context switching in and out of protected mode, and swapping cached pages. This is especially problematic for short-running, interactive functions in Function-as-a-Service (FaaS) platforms, where existing techniques to address enclave overheads are insufficient. We find FaaS functions can spend more time managing the enclave than executing application instructions. In this work, we propose a TEE/GC hybrid (TGh) protocol to enable confidential FaaS platforms. TGh moves computation out of the enclave onto the untrusted host using garbled circuits (GC), a cryptographic construction for secure function evaluation. Our approach retains the security guarantees of enclaves while avoiding the performance issues associated with enclave management instructions.

In this note we discuss Reed-Solomon codes with domain of definition within the unit circle of the complex extension $\mathbb C(F)$ of a Mersenne prime field $F$. Within this unit circle the interpolants of “real”, i.e. $F$-valued, functions are again almost real, meaning that their values can be rectified to a real representation at almost no extra cost. Second, using standard techniques for the FFT of real-valued functions, encoding can be sped up significantly. Due to the particularly efficient arithmetic of Mersenne fields, we expect these “almost native” Reed-Solomon codes to perform as native ones based on prime fields with high two-adicity, but less processor-friendly arithmetic.

This write-up summarizes the sampling analysis of the expander code from Brakedown [GLSTW21]. We elaborate their convexity argument for general linear expansion bounds, and we combine their approach with the one from Spielman [Sp96] to achieve asymptotic linear-time under constant field size. Choosing tighter expansion bounds we obtain more efficient parameters than [GLSTW21] for their 128 bit large field, reducing the encoding costs by 25% and beyond, and we provide a similar parameter set for the Mersenne prime field with modulus $p = 2^{31} - 1$, optimized by the combined Spielman-Brakedown approach.

Recent advances in the capabilities of large language models such as GPT-4 have spurred increasing concern about our ability to detect AI-generated text. Prior works have suggested methods of embedding watermarks in model outputs, by $\textit{noticeably}$ altering the output distribution. We ask: Is it possible to introduce a watermark without incurring $\textit{any detectable}$ change to the output distribution? To this end we introduce a cryptographically-inspired notion of undetectable watermarks for language models. That is, watermarks can be detected only with the knowledge of a secret key; without the secret key, it is computationally intractable to distinguish watermarked outputs from those of the original model. In particular, it is impossible for a user to observe any degradation in the quality of the text. Crucially, watermarks should remain undetectable even when the user is allowed to adaptively query the model with arbitrarily chosen prompts. We construct undetectable watermarks based on the existence of one-way functions, a standard assumption in cryptography.

With the emergence of Miner Extractable Value (MEV), block construction markets on blockchains have evolved into a competitive arena. Following Ethereum's transition from Proof of Work (PoW) to Proof of Stake (PoS), the Proposer Builder Separation (PBS) mechanism has emerged as the dominant force in the Ethereum block construction market. This paper presents an in-depth longitudinal study of the Ethereum block construction market, spanning from the introduction of PoS and PBS in September 2022 to May 2023. We analyze the market shares of builders and relays, their temporal changes, and the financial dynamics within the PBS system, including payments among builders and block proposers---commonly referred to as bribes. We introduce an MEV-time law quantifying the expected MEV revenue wrt. the time elapsed since the last proposed block. We provide empirical evidence that moments of crisis (e.g. the FTX collapse, USDC stablecoin de-peg) coincide with significant spikes in MEV payments compared to the baseline. Despite the intention of the PBS architecture to enhance decentralization by separating actor roles, it remains unclear whether its design is optimal. Implicit trust assumptions and conflicts of interest may benefit particular parties and foster the need for vertical integration. MEV-Boost was explicitly designed to foster decentralization, causing the side effect of enabling risk-free sandwich extraction from unsuspecting users, potentially raising concerns for regulators.