A natural approach to anonymous voting over Ethereum assumes that there is an off-chain aggregator that performs the following task. The aggregator receives valid signatures of YES/NO preferences from eligible voters and uses them to compute a zk-SNARK proof of the fact that the majority of voters have cast a preference for YES or NO. Then, the aggregator sends to the smart contract the zk-SNARK proof, the smart contract verifies the proof and can trigger an action (e.g., a transfer of funds). As the zk-SNARK proof guarantees anonymity, the privacy of the voters is preserved by attackers not colluding with the aggregator. Moreover, if the SNARK proof verification is efficient the GAS cost will be independent on the number of participating voters and signatures submitted by voters to the aggregator. In this paper we show that this naive approach to run referenda over Ethereum can incur severe security problems. We propose both mitigations and hardness results for achieving voting procedures in which the proofs submitted on-chain are either ZK or succinct.

Nearest neighbor search is a fundamental building-block for a wide range of applications. A privacy-preserving protocol for nearest neighbor search involves a set of clients who send queries to a remote database. Each client retrieves the nearest neighbor(s) to its query in the database without revealing any information about the query. To ensure database privacy, clients must learn as little as possible beyond the query answer, even if behaving maliciously by deviating from protocol. Existing protocols for private nearest neighbor search require heavy cryptographic tools, resulting in high computational and bandwidth overheads. In this paper, we present the first lightweight protocol for private nearest neighbor search. Our protocol is instantiated using two non-colluding servers, each holding a replica of the database. Our design supports an arbitrary number of clients simultaneously querying the database through the two servers. Each query consists of a single round of communication between the client and the two servers. No communication is required between the servers to answer queries. If at least one of the servers is non-colluding, we ensure that (1) no information is revealed on the client’s query, (2) the total communication between the client and the servers is sublinear in the database size, and (3) each query answer only leaks a small and bounded amount of information about the database to the client, even if the client is malicious. We implement our protocol and report its performance on real-world data. Our construction requires between 10 and 20 seconds of query latency over large databases of 10M feature vectors. Client overhead remained under 10 ms of processing time per query and less than 10 MB of communication.

Anonymous systems (e.g. anonymous cryptocurrencies and updatable anonymous credentials) often follow a construction template where an account can only perform a single anonymous action, which in turn potentially spawns new (and still single-use) accounts (e.g. UTXO with a balance to spend or session with a score to claim). Due to the anonymous nature of the action, no party can be sure which account has taken part in an action and, therefore, must maintain an ever-growing list of potentially unused accounts to ensure that the system keeps running correctly. Consequently, anonymous systems constructed based on this common template are seemingly not sustainable. In this work, we study the sustainability of ring-based anonymous systems, where a user performing an anonymous action is hidden within a set of decoy users, traditionally called a ``ring''. On the positive side, we propose a general technique for ring-based anonymous systems to achieve sustainability. Along the way, we define a general model of decentralised anonymous systems (DAS) for arbitrary anonymous actions, and provide a generic construction which provably achieves sustainability. As a special case, we obtain the first construction of anonymous cryptocurrencies achieving sustainability without compromising availability. We also demonstrate the generality of our model by constructing sustainable decentralised anonymous social networks. On the negative side, we show empirically that Monero, one of the most popular anonymous cryptocurrencies, is unlikely to be sustainable without altering its current ring sampling strategy. The main subroutine is a sub-quadratic-time algorithm for detecting used accounts in a ring-based anonymous system.

Sneakers were designated as the most counterfeited fashion item online, with three times more risk in a trade than any other fashion purchase. As the market expands, the current sneaker scene displays several vulnerabilities and trust flaws, mostly related to the legitimacy of assets or actors. In this paper, we investigate various blockchain-based mechanisms to address these large-scale trust issues. We argue that (i) pre-certified and tracked assets through the use of non-fungible tokens can ensure the genuine nature of an asset and authenticate its owner more effectively during peer-to-peer trading across a marketplace; (ii) a game-theoretic-based system with economic incentives for participating users can greatly reduce the rate of online fraud and address missed delivery deadlines; (iii) a decentralized dispute resolution system biased in favour of an honest party can solve potential conflicts more reliably.

We introduce the Clockwork Finance Framework (CFF), a general purpose, formal verification framework for mechanized reasoning about the economic security properties of composed decentralized-finance (DeFi) smart contracts. CFF features three key properties. It is contract complete, meaning that it can model any smart contract platform and all its contracts—Turing complete or otherwise. It does so with asymptotically constant model overhead. It is also attack-exhaustive by construction, meaning that it can automatically and mechanically extract all possible economic attacks on users’ cryptocurrency across modeled contracts. Thanks to these properties, CFF can support multiple goals: economic security analysis of contracts by developers, analysis of DeFi trading risks by users, fees UX, and optimization of arbitrage opportunities by bots or miners. Because CFF offers composability, it can support these goals with reasoning over any desired set of potentially interacting smart contract models. We instantiate CFF as an executable model for Ethereum contracts that incorporates a state-of-the-art deductive verifier. Building on previous work, we introduce extractable value (EV), a new formal notion of economic security in composed DeFi contracts that is both a basis for CFF and of general interest. We construct modular, human-readable, composable CFF models of four popular, deployed DeFi protocols in Ethereum: Uniswap, Uniswap V2, Sushiswap, and MakerDAO, representing a combined 24 billion USD in value as of March 2022. We use these models along with some other common models such as flash loans, airdrops and voting to show experimentally that CFF is practical and can drive useful, data-based EV-based insights from real world transaction activity. Without any explicitly programmed attack strategies, CFF uncovers on average an expected $56 million of EV per month in the recent past.

Proof-of-stake systems require stakers to lock up their funds in order to participate in consensus validation. This leads to capital inefficiency, as locked capital cannot be invested in Decentralized Finance (DeFi). Liquid staking rewards stakers with fungible tokens in return for staking their assets. These fungible tokens can in turn be reused in the DeFi economy. However, liquid staking introduces unexpected risks, as all delegated stake is now fungible. This exacerbates the already existing Principal–Agent problem faced during any delegation, in which the interests of the delegator (the Principal) are not aligned with the interests of the validator (the Agent). In this paper, we study the Principal–Agent problem in the context of liquid staking. We highlight the dilemma between the choice of proportional representation (having one's stake delegated to one's validator of choice) and fair punishment (being economically affected only when one's choice is misinformed). We put forth an attack illustrating that these two notions are fundamentally incompatible in an adversarial setting. We then describe the mechanism of exempt delegations, used by some staking systems today, and devise a precise formula for quantifying the correct choice of exempt delegation which allows balancing the two conflicting virtues in the rational model.

We consider leader-based Byzantine state machine replication, a.k.a. "BFT", under partial synchrony. We provide a generic solution enabling to match simultaneously, for the first time, three arguably gold standards of BFT: in two phases, with a responsive view change and a linear complexity per view. It is based on a new threshold primitive, which we call Proofs of non-Supermajority (or PnS for short). A PnS system enables players, each with an input number, to report their input to a leader, with extra hints enabling their efficient aggregation. Out of a threshold number $k$ ($=\,\! 2t{\,+\,}1$) of such reports, calling $v_\mathrm{max}$ the highest reported value, the leader can efficiently build a short proof that a threshold number of $k-t$ honest players have their inputs lower than this $v_\mathrm{max}$. As highlighted in the state of the art BFT [Abraham et al. Podc'23], any of our lightweight implementations of PnS can be plugged in their BFT, or the one of [Gelashvili et al FC'22], to bring down their complexities from quadratic to linear. Previous BFTs implicitely implemented PnS by either (i) having the leader multicasting the $k$ signed reports, so this had quadratic communication complexity, or (ii) multicasting an aggregate signature on the reports, with verification complexity of $k+1$ pairings, so this had a total quadratic computation complexity. To match our linear complexity claim, we introduce a very simple constant-sized and constant-verification implementation of PnS, built from any threshold (or multi-) signature scheme. We then bring further optimizations by introducing the following tools of possible independent interest: (1) a simple and general optimization to BFTs, which applies to any view without a hidden lock. It removes the need for sending and verifying a PnS. Previous optimizations applied to a narrower set of views, essentially, those for which the leader of the previous one was honest and enjoyed synchrony; (2) a simple compiler from any multisignature scheme, into an aggregate signature scheme of a special-purpose type. It operates over tagged messages, each key can sign at most one message with a given tag.

Post-Quantum Cryptography (PQC) defines cryptographic algorithms designed to resist the advent of the quantum computer. Most public-key cryptosystems today are vulnerable to quantum attackers, so a global-scale transition to PQC is expected. As a result, several entities foment efforts in PQC standardization, research, development, creation of Work Groups (WGs), and issuing adoption recommendations. However, there is a long road to broad PQC adoption in practice. This position paper describes why migrating to PQC is necessary and gathers evidence that the ``hybrid mode'' can help the migration process. Finally, it stresses that there are risks yet to be considered by the literature. Quantum-safe protocols are being evaluated, but more attention (and awareness) is needed for the software and protocols at the application layer. Lastly, this position paper gives further recommendations for a smother PQC migration.

Unclonable cryptography builds primitives that enjoy some form of unclonability, such as quantum money, software copy protection, and bounded execution programs. These are impossible in the classical model as classical data is inherently clonable. Quantum computing, with its no-cloning principle, offers a solution. However, it is not enough to realize bounded execution programs; these require one-time memory devices that self-destruct after a single data retrieval query. Very recently, a new no-cloning technology has been introduced [Eurocrypt'22], showing that unclonable polymers---proteins---can be used to build bounded-query memory devices and unclonable cryptographic applications. In this paper, we investigate the relation between these two technologies; whether one can replace the other, or complement each other such that combining them brings the best of both worlds. Towards this goal, we review the quantum and unclonable polymer models, and existing unclonable cryptographic primitives. Then, we discuss whether these primitives can be built using the other technology, and show alternative constructions and notions when possible. We also offer insights and remarks for the road ahead. We believe that this study will contribute in advancing the field of unclonable cryptography on two fronts: developing new primitives, and realizing existing ones using new constructions.

Systems with distributed trust have attracted growing research attention and seen increasing industry adoptions. In these systems, critical secrets are distributed across N servers, and computations are performed privately using secure multi-party computation (SMPC). Authentication for these distributed-trust systems faces two challenges. The first challenge is ease-of-use. Namely, how can an authentication protocol maintain its user experience without sacrificing security? To avoid a central point of attack, a client needs to authenticate to each server separately. However, this would require the client to authenticate N times for each authentication factor, which greatly hampers usability. The second challenge is privacy, as the client’s sensitive profiles are now exposed to all N servers under different trust domains, which creates N times the attack surface for the profile data. We present MPCAuth, a multi-factor authentication system for distributed-trust applications that address both challenges. Our system enables a client to authenticate to N servers independently with the work of only one authentication. In addition, our system is profile hiding, meaning that the client’s authentication profiles such as her email username, phone number, passwords, and biometric features are not revealed unless all servers are compromised. We propose secure and practical protocols for an array of widely adopted authentication factors, including email passcodes, SMS messages, U2F, security questions/passwords, and biometrics. Our system finds practical applications in the space of cryptocurrency custody and collaborative machine learning, and benefits future adoptions of distributed-trust applications.

A whistleblower is a person who leaks sensitive information on a prominent individual or organisation engaging in an unlawful or immoral activity. Whistleblowing has the potential to mitigate corruption and fraud by identifying the misuse of capital. In extreme cases whistleblowing can also raise awareness about unethical practices to individuals by highlighting dangerous working conditions. Obtaining and sharing the sensitive information associated with whistleblowing can carry great risk to the individual or party revealing the data. In this paper we extend the notion of timed-release encryption to include a new security property which we term implicit authentication, with the goal of making the practice of whistleblowing safer. We formally define the new primitive of timed-release encryption with implicit authentication (TRE-IA), providing rigorous game-base definitions. We then build a practical TRE-IA construction that satisfies the security requirements of this primitive, using repeated squaring in an RSA group, and the RSA-OAEP encryption scheme. We formally prove our construction secure and provide a performance analysis of our implementation in Python along with recommendations for practical deployment and integration with an existing whistleblowing tool SecureDrop.

Computing the quantile of a massive data stream has been a crucial task in networking and data management. However, existing solutions assume a centralized model where one data owner has access to all data. In this paper, we put forward a study of secure quantile aggregation between private data streams, where data streams owned by different parties would like to obtain a quantile of the union of their data without revealing anything else about their inputs. To this end, we designed efficient cryptographic protocols that are secure in the semi-honest setting as well as the malicious setting. By incorporating differential privacy, we further improve the efficiency by 1.1× to 73.1×. We implemented our protocol, which shows practical efficiency to aggregate real-world data streams efficiently.

Millions of smart contracts have been deployed onto Ethereum for providing various services, whose functions can be invoked. For this purpose, the caller needs to know the function signature of a callee, which includes its function id and parameter types. Such signatures are critical to many applications focusing on smart contracts, e.g., reverse engineering, fuzzing, attack detection, and profiling. Unfortunately, it is challenging to recover the function signatures from contract bytecode, since neither debug information nor type information is present in the bytecode. To address this issue, prior approaches rely on source code, or a collection of known signatures from incomplete databases or incomplete heuristic rules, which, however, are far from adequate and cannot cope with the rapid growth of new contracts. In this paper, we propose a novel solution that leverages how functions are handled by Ethereum virtual machine (EVM) to automatically recover function signatures. In particular, we exploit how smart contracts determine the functions to be invoked to locate and extract function ids, and propose a new approach named type-aware symbolic execution (TASE) that utilizes the semantics of EVM operations on parameters to identify the number and the types of parameters. Moreover, we develop SigRec , a new tool for recovering function signatures from contract bytecode without the need of source code and function signature databases. The extensive experimental results show that SigRec outperforms all existing tools, achieving an unprecedented 98.7 percent accuracy within 0.074 seconds. We further demonstrate that the recovered function signatures are useful in attack detection, fuzzing and reverse engineering of EVM bytecode.

This note provides an update on the Open Specification Language (OSL) circuit compiler. OSL is a language based on predicate logic which is amenable to compilation to arithmetic constraint systems for use in constructing (zk-)SNARKs. This system provides an alternative to universal zk-VMs and low level ad hoc constructions of arithmetic constraint systems, which is potentially more efficient than universal zk-VMs but more cost effective as a development approach than low level ad hoc constructions.

AI-as-a-Service has emerged as an important trend for supporting the growth of the digital economy. Digital service providers make use of their vast amount of user data to train AI models (such as image recognitions, financial modelling and pandemic modelling etc.) and offer them as a service on the cloud. While there are convincing advantages for using such third-party models, the fact that users need to upload their data to the cloud is bound to raise serious privacy concerns, especially in the face of increasingly stringent privacy regulations and legislations. To promote the adoption of AI-as-a-Service while addressing the privacy issues, we propose a practical approach for constructing privacy-enhanced neural networks by designing an efficient implementation of fully homomorphic encryption. With this approach, an existing neural network can be converted to process FHE-encrypted data and produce encrypted output which are only accessible by the model users, and more importantly, within an operationally acceptable time (e.g. within 1 second for facial recognition in typical border control systems). Experimental results show that in many practical tasks such as facial recognition, text classification and so on, we obtained the state-of-the-art inference accuracy in less than one second on a 16 cores CPU.

Privacy computing is a collection of a series of technical systems that intersect and integrate many disciplines such as cryptography, statistics, artificial intelligence, and computer hardware. On the premise of not exposing the original data, it can realize the fusion, sharing, circulation and calculation of data and its value in a manageable, controllable and measurable way. In the case of ensuring that the data is not leaked, it can achieve the purpose of making the data available and invisible, as well as the conversion and release of data value. We propose a privacy computing algorithm based on polynomial operations based on the unsolvable problem of high-degree polynomial modulo n. Encryptors can encrypt their own private information to generate ciphertext that can be calculated. This ciphertext can support any integer calculation including addition, multiplication, power calculation, etc. Different ciphertexts generated by the same key are fully homomorphic, and addition and multiplication operations can be performed between these ciphertexts. All calculations in our encryption system are carried out with polynomials as the medium, so the calculation efficiency is guaranteed. Our ciphertext can be provided to a third party for processing without revealing the encryption party's key and secret information.

This note describes two pairing-friendly curves that embed ed25519, of different bit security levels. Our search is not novel; it follows the standard recipe of the Cocks-Pinch method. We implemented these two curves on arkworks-rs. This note is intended to document how the parameters are being generated and how to implement these curves in arkworks-rs 0.4.0, for further reference. We name the two curves as Yafa-108 and Yafa-146: - Yafa-108 is estimated to offer 108-bit security, which we parameterized to match the 103-bit security of BN254 - Yafa-146 is estimated to offer 146-bit security, which we parameterized to match the 132-bit security of BLS12-446 or 123-bit security of BLS12-381 We use these curves as an example to demonstrate two things: - The "elastic" zero-knowledge proof, Gemini (EUROCRYPT '22), is more than being elastic, but it is more curve-agnostic and hardware-friendly. - The cost of nonnative field arithmetics can be drastic, and the needs of application-specific curves may be inherent. This result serves as evidence of the necessity of EIP-1962, and the insufficiency of EIP-2537.

Fault injection attacks have caused implementations to behave unexpectedly, resulting in a spectacular bypass of security features and even the extraction of cryptographic keys. Clearly, developers want to ensure the robustness of the software against faults and eliminate production weaknesses that could lead to exploitation. Several fault simulators have been released that promise cost-effective evaluations against fault attacks. In this paper, we set out to discover how suitable such tools are, for a developer who wishes to create robust software against fault attacks. We found four open-source fault simulators that employ different techniques to navigate faults, which we objectively compare and discuss their benefits and drawbacks. Unfortunately, none of the four open-source fault simulators employ artificial intelligence (AI) techniques. However, AI was successfully applied to improve the fault simulation of cryptographic algorithms, though none of these tools is open source. We suggest improvements to open-source fault simulators inspired by the AI techniques used by cryptographic fault simulators.

This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System. Unlike traditional methods, BlockGPT is designed to offer an unrestricted search space and does not rely on predefined rules or patterns, enabling it to detect a broader range of anomalies. We demonstrate the effectiveness of BlockGPT through its use as an anomaly detection tool for Ethereum transactions. In our experiments, it effectively identifies abnormal transactions among a dataset of 68M transactions and has a batched throughput of 2284 trans- actions per second on average. Our results show that, BlockGPT identifies abnormal transactions by ranking 49 out of 124 attacks among the top-3 most abnormal transactions interacting with their victim contracts. This work makes contributions to the field of blockchain transaction analysis by introducing a custom data encoding compatible with the transformer architecture, a domain-specific tokenization technique, and a tree encoding method specifically crafted for the Ethereum Virtual Machine (EVM) trace representation.

A decisive contribution to the all-embracing protection of cryptographic software, especially on embedded devices, is the protection against SCA attacks. Masking countermeasures can usually be integrated into the software during the design phase. In theory, this should provide reliable protection against such physical attacks. However, the correct application of masking is a non-trivial task that often causes even experts to make mistakes. In addition to human-caused errors, micro-architectural CPU effects can lead even a seemingly theoretically correct implementation to fail to satisfy the desired level of security in practice. This originates from different components of the underlying CPU which complicates the tracing of leakage back to a particular source and hence avoids making general and device-independent statements about its security. PROLEAD has recently been presented at CHES 2022 and has originally been developed as a simulation-based tool to evaluate masked hardware designs. In this work, we adapt PROLEAD for the evaluation of masked software, and enable the transfer of the already known benefits of PROLEAD into the software world. These include (1) evaluation of larger designs compared to the state of the art, e.g. a full AES masked implementation, and (2) formal verification under our new generic leakage model for CPUs. Concretely, we formalize leakages, observed across different CPU architectures, into a generic abstraction model that includes all these leakages and is therefore independent of a specific CPU design. Our resulting tool PROLEAD_SW allows to provide a formal statement on the security based on the derived generic model. As a concrete result, using PROLEAD_SW we evaluated the security of several publicly available masked software implementations in our new generic leakage model and reveal multiple vulnerabilities.

Masking is a popular secret-sharing technique that is used to protect cryptographic implementations against physical attacks like differential power analysis. So far, most research in this direction has focused on finding efficient Boolean masking schemes for well-known symmetric cryptographic algorithms like AES and Keccak. However, especially with the advent of post-quantum cryptography (PQC), arithmetic masking has received increasing attention from the research community. In practice, many PQC algorithms require a combination of arithmetic and Boolean masking, which makes the search for secure and efficient conversion algorithms between these domains (A2B/B2A) an interesting but very challenging research topic. While there already exist lots of tools that can help with the formal verification of Boolean masked implementations, the same cannot be said about arithmetic masking and accompanying mask conversion algorithms. In this work, we demonstrate the first formal verification approach for (any-order) Boolean and arithmetic masking which can be applied to both hardware and software, while considering side-effects such as glitches and transitions. First, we show how a formal verification approach for Boolean masking can be used in the context of arithmetic masking such that we can verify A2B/B2A conversions for arbitrary masking orders. We investigate various conversion algorithms in hardware and software, and point out several new findings such as glitch-based issues for straightforward implementations of [CGV14]-A2B in hardware, transition-based leakage in Goubin-A2B in software, and more general implementation pitfalls when utilizing common optimization techniques in PQC. We provide the first formal analysis of table-based A2Bs from a probing security perspective and point out that they might not be easy to implement securely on processors that use of memory buffers or caches.

As zero-knowledge proofs gain increasing adoption, the cryptography community has designed domain-specific languages (DSLs) that facilitate the construction of zero-knowledge proofs (ZKPs). Many of these DSLs, such as Circom, facilitate the construction of arithmetic circuits, which are essentially polynomial equations over a finite field. In particular, given a program in a zero-knowledge proof DSL, the compiler automatically produces the corresponding arithmetic circuit. However, a common and serious problem is that the generated circuit may be underconstrained, either due to a bug in the program or a bug in the compiler itself. Underconstrained circuits admit multiple witnesses for a given input, so a malicious party can generate bogus witnesses, thereby causing the verifier to accept a proof that it should not. Because of the increasing prevalence of such arithmetic circuits in blockchain applications, several million dollars worth of cryptocurrency have been stolen due to underconstrained arithmetic circuits. Motivated by this problem, we propose a new technique for finding ZKP bugs caused by underconstrained polynomial equations over finite fields. Our method performs semantic reasoning over the finite field equations generated by the compiler to prove whether or not each signal is uniquely determined by the input. Our proposed approach combines SMT solving with lightweight uniqueness inference to effectively reason about underconstrained circuits. We have implemented our proposed approach in a tool called $\mathbf{\mathsf{QED}^2}$ and evaluate it on 163 Circom circuits. Our evaluation shows that $\mathbf{\mathsf{QED}^2}$ can successfully solve 70\% of these benchmarks, meaning that it either verifies the uniqueness of the output signals or finds a pair of witnesses that demonstrate non-uniqueness of the circuit. Furthermore, $\mathbf{\mathsf{QED}^2}$ has found 8 previously unknown vulnerabilities in widely-used circuits.

This paper introduces a secure and efficient hybrid scheme based on polar codes, called as HES-PC. The proposed HES-PC contains of two other mechanisms: a key encapsulation mechanism based on polar codes, called as KEM-PC, a data encapsulation mechanism based on polar codes, called as DEM-PC. In fact, the symmetric key is exchanged between the legitimate partners by exploiting the KEM-PC. Also, secure polar encoding/successive cancelation (SC) decoding is enhanced between the honest parties by using DEM-PC.

Modern Singular Value Decomposition (SVD) computation dates back to the 1960s when the basis for the eigensystem package and linear algebra package routines was created. Since then, SVD has gained attraction and been widely applied in various scenarios, such as recommendation systems and principal component analyses. Federated SVD has recently emerged, where different parties could collaboratively compute SVD without exchanging raw data. Besides its inherited privacy protection, noise injection could be utilized to further increase the privacy guarantee of this privacy-friendly technique. This paper advances the state-of-science by improving an existing Federated SVD scheme with two-fold contributions. First, we revise its privacy guarantee in terms of Differential Privacy, the de-facto data privacy standard of the 21st century. Second, we increase its utility by reducing the added noise, which is achieved by employing Secure Aggregation, a cryptographic technique to prevent information leakage. Using a recommendation system use-case with real-world data, we demonstrate that our scheme outperforms the state-of-the-art Federated SVD solution.

Fully homomorphic encryption (FHE) is a powerful cryptographic technique allowing to perform computation directly over encrypted data. Motivated by the overhead induced by the homomorphic ciphertexts during encryption and transmission, the transciphering technique, consisting in switching from a symmetric encryption to FHE encrypted data was investigated in several papers. Different stream and block ciphers were evaluated in terms of their "FHE-friendliness", meaning practical implementations costs while maintaining sufficient security levels. In this work, we present a first evaluation of hash functions in the homomorphic domain, based on well-chosen block ciphers. More precisely, we investigate the cost of transforming PRINCE, SIMON, SPECK, and LowMC, a set of lightweight block-ciphers into secure hash primitives using well-established hash functions constructions based on block-ciphers, and provide evaluation under bootstrappable FHE schemes. We also motivate the necessity of practical homomorphic evaluation of hash functions by providing several use cases in which the integrity of private data is also required. In particular, our hash constructions can be of significant use in a threshold-homomorphic based protocol for the single secret leader election problem occurring in blockchains with Proof-of-stake consensus. Our experiments showed that using a TFHE implementation of a hash function, we are able to achieve practical runtime, and appropriate security levels (e.g., for PRINCE it takes 1.28 minutes to obtain a 128 bits of hash).

This paper introduces Ibex, an advertising system that reduces the amount of data that is collected on users while still allowing advertisers to bid on real-time ad auctions and measure the effectiveness of their ad campaigns. Specifically, Ibex addresses an issue in recent proposals such as Google’s Privacy Sandbox Topics API in which browsers send information about topics that are of interest to a user to advertisers and demand-side platforms (DSPs). DSPs use this information to (1) determine how much to bid on the auction for a user who is interested in particular topics, and (2) measure how well their ad campaign does for a given audience (i.e., measure conversions). While Topics and related proposals reduce the amount of user information that is exposed, they still reveal user preferences. In Ibex, browsers send user information in an encrypted form that still allows DSPs and advertisers to measure conversions, compute aggregate statistics such as histograms about users and their interests, and obliviously bid on auctions without learning for whom they are bidding. Our implementation of Ibex shows that creating histograms is 1.7–2.5× more expensive for browsers than disclosing user information, and Ibex’s oblivious bidding protocol can finish auctions within 550 ms. We think this makes Ibex capable of preserving a good experience while improving user privacy.

Blockchain security relies on incentives to ensure participants, called miners, cooperate and behave as the protocol dictates. Such protocols have a security threshold – a miner whose relative computational power is larger than the threshold can deviate to improve her revenue. Moreover, blockchain participants can behave in a petty compliant manner: usually follow the protocol, but deviate to increase revenue when deviation cannot be distinguished externally from the prescribed behavior. The effect of petty compliant miners on the security threshold of blockchains is not well understood. Due to the complexity of the analysis, it remained an open question since Carlsten et al. identified it in 2016. In this work, we use deep Reinforcement Learning (RL) to analyze how a rational miner performs selfish mining by deviating from the protocol to maximize revenue when petty compliant miners are present. We find that a selfish miner can exploit petty compliant miners to increase her revenue by bribing them. Our method reveals that the security threshold is lower when petty compliant miners are present. In particular, with parameters estimated from the Bitcoin blockchain, we find the threshold drops from the known value of 25% to only 21% (or 19%) when 50% (or 75%) of the other miners are petty compliant. Hence, our deep RL analysis puts the open question to rest; the presence of petty compliant miners exacerbates a blockchain’s vulnerability to selfish mining and is a major security threat.

Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many important techniques for improving security and privacy, including homomorphic encryption and post-quantum cryptography. While promising, these techniques have received limited use due to their extreme overheads of running on general-purpose machines. In this paper, we present a novel vector Instruction Set Architecture (ISA) and microarchitecture for accelerating the ring-based computations of RLWE. The ISA, named B512, is developed to meet the needs of ring processing workloads while balancing high-performance and general-purpose programming support. Having an ISA rather than fixed hardware facilitates continued software improvement post-fabrication and the ability to support the evolving workloads. We then propose the ring processing unit (RPU), a high-performance, modular implementation of B512. The RPU has native large word modular arithmetic support, capabilities for very wide parallel processing, and a large capacity high-bandwidth scratchpad to meet the needs of ring processing. We address the challenges of programming the RPU using a newly developed SPIRAL backend. A configurable simulator is built to characterize design tradeoffs and quantify performance. The best performing design was implemented in RTL and used to validate simulator performance. In addition to our characterization, we show that a RPU using 20.5mm2 of GF 12nm can provide a speedup of 1485x over a CPU running a 64k, 128-bit NTT, a core RLWE workload

In this paper, we propose a non-interactive privacy-preserving naive Bayes classifier from leveled fully homomorphic encryption schemes. The classifier runs on a server that is also the model’s owner (modeler), whose input is the encrypted data from a client. The classifier produces encrypted classification results, which can only be decrypted by the client, while the modelers model is only accessible to the server. Therefore, the classifier does not leak any privacy on either the servers model or the clients data and results. More importantly, the classifier does not require any interactions between the server and the client during the classification phase. The main technical ingredient is an algorithm that computes the maximum index of an encrypted array homomorphically without any interactions. The proposed classifier is implemented using HElib. Experiments show the accuracy and efficiency of our classifier. For instance, the average cost can achieve about 34ms per sample for a real data set in UCI Machine Learning Repository with the security parameter about 100 and accuracy about 97%.

ISO 15118 enables charging and billing of Electric Vehicles (EVs) without user interaction by using locally installed cryptographic credentials that must be secure over the long lifetime of vehicles. In the dawn of quantum computers, Post-Quantum Cryptography (PQC) needs to be integrated into the EV charging infrastructure. In this paper, we propose QuantumCharge, a PQC extension for ISO 15118, which includes concepts for migration, crypto-agility, verifiable security, and the use of PQC-enabled hardware security modules. Our prototypical implementation and the practical evaluation demonstrate the feasibility, and our formal analysis shows the security of QuantumCharge, which thus paves the way for secure EV charging infrastructures of the future.

The rising issues of harassment, exploitation, corruption, and other forms of abuse have led victims to seek comfort by acting in unison against common perpetrators (e.g., #MeToo movement). One way to curb these issues is to install allegation escrow systems that allow victims to report such incidents. The escrows are responsible for identifying victims of a common perpetrator and taking the necessary action to bring justice to them. However, users hesitate to participate in these systems due to the fear of such sensitive reports being leaked to perpetrators, who may further misuse them. Thus, to increase trust in the system, cryptographic solutions are being designed to realize secure allegation escrow (SAE) systems. In the work of Arun et al. (NDSS'20), which presents the state-of-the-art solution, we identify attacks that can leak sensitive information and compromise victim privacy. We also report issues present in prior works that were left unidentified. To arrest all these breaches, we put forth an SAE system that prevents the identified attacks and retains the salient features from all prior works. The cryptographic technique of secure multi-party computation (MPC) serves as the primary underlying tool in designing our system. At the heart of our system lies a new duplicity check protocol and an improved matching protocol. We also provide additional features such as allegation modification and deletion, which were absent in the state of the art. To demonstrate feasibility, we benchmark the proposed system with state-of-the-art MPC protocols and report the cost of processing an allegation. Different settings that affect system performance are analyzed, and the reported values showcase the practicality of our solution.

Encryption alone is not enough for secure end-to-end encrypted messaging: a server must also honestly serve public keys to users. Key transparency has been presented as an efficient solution for detecting (and hence deterring) a server that attempts to dishonestly serve keys. Key transparency involves two major components: (1) a username to public key mapping, stored and cryptographically committed to by the server, and, (2) an out-of-band consistency protocol for serving short commitments to users. In the setting of real-world deployments and supporting production scale, new challenges must be considered for both of these components. We enumerate these challenges and provide solutions to address them. In particular, we design and implement a memory-optimized and privacy-preserving verifiable data structure for committing to the username to public key store. To make this implementation viable for production, we also integrate support for persistent and distributed storage. We also propose a future-facing solution, termed ''compaction'', as a mechanism for mitigating practical issues that arise from dealing with infinitely growing server data structures. Finally, we implement a consensusless solution that achieves the minimum requirements for a service that consistently distributes commitments for a transparency application, providing a much more efficient protocol for distributing small and consistent commitments to users. This culminates in our production-grade implementation of a key transparency system (Parakeet) which we have open-sourced, along with a demonstration of feasibility through our benchmarks.

Privacy enhancing technologies (PETs) have been proposed as a way to protect the privacy of data while still allowing for data analysis. In this work, we focus on Fully Homomorphic Encryption (FHE), a powerful tool that allows for arbitrary computations to be performed on encrypted data. FHE has received lots of attention in the past few years and has reached realistic execution times and correctness. More precisely, we explain in this paper how we apply FHE to tree-based models and get state-of-the-art solutions over encrypted tabular data. We show that our method is applicable to a wide range of tree-based models, including decision trees, random forests, and gradient boosted trees, and has been implemented within the Concrete-ML library, which is open-source at https://github.com/zama-ai/concrete-ml. With a selected set of use-cases, we demonstrate that our FHE version is very close to the unprotected version in terms of accuracy.

Zero-knowledge proof (ZKP) mixers are one of the most widely used blockchain privacy solutions, operating on top of smart contract-enabled blockchains. We find that ZKP mixers are tightly intertwined with the growing number of Decentralized Finance (DeFi) attacks and Blockchain Extractable Value (BEV) extractions. Through coin flow tracing, we discover that 205 blockchain attackers and 2,595 BEV extractors leverage mixers as their source of funds, while depositing a total attack revenue of 412.87M USD. Moreover, the US OFAC sanctions against the largest ZKP mixer, Tornado.Cash, have reduced the mixer’s daily deposits by more than 80%. Further, ZKP mixers advertise their level of privacy through a so-called anonymity set size, which similarly to $k$-anonymity allows a user to hide among a set of $k$ other users. Through empirical measurements, we, however, find that these anonymity set claims are mostly inaccurate. For the most popular mixers on Ethereum (ETH) and Binance Smart Chain (BSC), we show how to reduce the anonymity set size on average by 27.34% and 46.02% respectively. Our empirical evidence is also the first to suggest a differing privacy-predilection of users on ETH and BSC. State-of-the-art ZKP mixers are moreover interwoven with the DeFi ecosystem by offering anonymity mining (AM) incentives, i.e., users receive monetary rewards for mixing coins. However, contrary to the claims of related work, we find that AM does not necessarily improve the quality of a mixer’s anonymity set. Our findings indicate that AM attracts privacy-ignorant users, who then do not contribute to improving the privacy of other mixer users.

Using secure multiparty computation (MPC), organizations which own sensitive data (e.g., in healthcare, finance or law enforcement) can train machine learning models over their joint dataset without revealing their data to each other. At the same time, secure computation restricts operations on the joint dataset, which impedes computation to assess its quality. Without such an assessment, deploying a jointly trained model is potentially illegal. Regulations, such as the European Union's General Data Protection Regulation (GDPR), require organizations to be legally responsible for the errors, bias, or discrimination caused by their machine learning models. Hence, testing data quality emerges as an indispensable step in secure collaborative learning. However, performing distribution testing is prohibitively expensive using current techniques, as shown in our experiments. We present HOLMES, a protocol for performing distribution testing efficiently. In our experiments, compared with three non-trivial baselines, HOLMES achieves a speedup of more than 10x for classical distribution tests and up to 10^4x for multidimensional tests. The core of HOLMES is a hybrid protocol that integrates MPC with zero-knowledge proofs and a new ZK-friendly and naturally oblivious sketching algorithm for multidimensional tests, both with significantly lower computational complexity and concrete execution costs.

Data outsourcing is a solution aimed at addressing the security and reliability issues of data storage by ensuring professional handling of the data. The growing use of outsourcing is causing concern among users due to the lack of assurance regarding the security and reliability of data stored on servers. To address these issues, some attempts have been made to implement Secret Sharing-based Data Outsourcing (SSDO) schemes. The low efficiency of these schemes led researchers to use an index server (IS). However, IS are susceptible to frequency analysis. Bucket-Chain $B^+Tree$ ($BCB^+Tree$) was proposed to tackle the frequency analysis in the current Index Server Secret Sharing-based Data Outsourcing (ISSDO) schemes. Nevertheless, this scheme works very well when the data is discrete with a limited range. Otherwise, the scheme's efficiency declines significantly as it has to store one index in each bucket and the number of buckets rises significantly, rendering the use of an IS useless. In this paper, a new data structure is proposed to store the indexes in IS to mitigate this efficiency concern. Briefly, the domain of values is divided into several segments, and indexes of values in each segment are stored inside a $Shard$. Additionally, a data outsourcing scheme has been presented based on the proposed data structure. It can withstand collaboration from up to $k-1$ dishonest servers even if they have access to the IS.

Liquidations in DeFi are both a blessing and a curse — whereas liquidations prevent lenders from capital loss, they simultaneously lead to liquidation spirals and system-wide failures. Since most lending and borrowing protocols assume liquidations are indispensable, there is an increased interest in alternative constructions that prevent immediate systemic-failure under uncertain circumstances. In this work, we introduce reversible call options, a novel financial primitive that enables the seller of a call option to terminate it before maturity. We apply reversible call options to lending in DeFi and devise Miqado, a protocol for lending platforms to replace the liquidation mechanisms. To the best of our knowledge, Miqado is the first protocol that actively mitigates liquidations to reduce the risk of liquidation spirals. Instead of selling collateral, Miqado incentivizes external entities, so-called supporters, to top-up a borrowing position and grant the borrower additional time to rescue the debt. Our simulation shows that Miqado reduces the amount of liquidated collateral by 89.82% in a worst-case scenario.

The historical domain of information hiding is alternatively used nowadays for communication security. Maybe the oldest and certainly one of the most studied field that falls in this category is steganography. Within the current paper, we focus on image steganography techniques in the case of the JPEG format. We propose a corrected and optimized version of the J3 stegosystem which, to the best of our knowledge and as shown throughout the paper, may be considered a very good choice in terms of security and efficiency as compared to current solutions. We reconstruct the entire J3 algorithm (pre-processing, message insertion and extraction) and detail all the modifications. We also present implementation optimizations and cover all practical cases while still using the maximum image insertion capacity.

The growing popularity of cloud storage has brought attention to critical need for preventing information leakage from cloud access patterns. To this end, recent efforts have extended Oblivious RAM (ORAM) to the cloud environment in the form of Oblivious Store. However, its impracticality due to the use of probability encryption with fake accesses to obfuscate the access pattern, as well as the security requirements of conventional obliviousness designs, which hinder cloud interests in improving storage utilization by removing redundant data among cross-users, limit its effectiveness. Thus, we propose a practical Oblivious Store, PEO-Store, which integrates the obliviousness property into the cloud while removing redundancy without compromising security. Unlike conventional schemes, PEO-Store randomly selects a delegate for each client to communicate with the cloud, breaking the mapping link between a valid access pattern sequence and a specific client. Each client encrypts their data and shares it with selected delegates, who act as intermediaries with the cloud provider. This design leverages non-interactive zero-knowledge-based redundancy detection, discrete logarithm problem-based key sharing, and secure time-based delivery proof to protect access pattern privacy and accurately identify and remove redundancy in the cloud. The theoretical proof demonstrates that the probability of identifying the valid access pattern with a specific user is negligible in our design. Experimental results show that PEO-Store outperforms state-of-the-art methods, achieving an average throughput of up to 3 times faster and saving 74% of storage space.

In 2008 Satoshi wrote the first permissionless consensus protocol, known as Nakamoto Consensus (NC), and implemented in Bitcoin. A large body of research was dedicated since to modify and extend NC, in various aspects: speed, throughput, energy consumption, computation model, and more. One line of work focused on alleviating the security-speed tradeoff which NC suffers from by generalizing Satoshi's blockchain into a directed acyclic graph of blocks, a block DAG. Indeed, the block creation rate in Bitcoin must be suppressed in order to ensure that the block interval is much longer than the worst case latency in the network. In contrast, the block DAG paradigm allows for arbitrarily high block creation rate and block sizes, as long as the capacity of nodes and of the network backbone are not exceeded. Still, these protocols, as well as other permissionless protocols, assume an a priori bound on the worst case latency, and hardcode a corresponding parameter in the protocol. Confirmation times then depend on this worst case bound, even when the network is healthy and messages propagate very fast. In this work we set out to alleviate this constraint, and create the first permissionless protocol which contains no a priori in-protocol bound over latency. DAG-KNIGHT is thus responsive to network conditions, while tolerating a corruption of up to 50% of the computational power (hashrate) in the network. To circumvent an impossibility result by Pass and Shi, we require that the client specifies locally an upper bound over the maximum adversarial recent latency in the network. DAG-KNIGHT is an evolution of the PHANTOM paradigm, which is a parameterized generalization of NC.

As of 28 January 2022, Filecoin is ranked as the first capitalized storage-oriented cryptocurrency. In this system, miners dedicate their storage space to the network and verify transactions to earn rewards. Nowadays, Filecoin's network capacity has surpassed 15 exbibytes. In this paper, we propose three temporary block withholding attacks to challenge Filecoin's expected consensus (EC). Specifically, we first deconstruct EC following old-fashioned methods (which have been widely developed since 2009) to analyze the advantages and disadvantages of EC's design. We then present three temporary block withholding schemes by leveraging the shortcomings of EC. We build Markov Decision Process (MDP) models for the three attacks to calculate the adversary's gains. We develop Monte Carlo simulators to mimic the mining strategies of the adversary and other miners and indicate the impacts of the three attacks on expectation. As a result, we show that our three attacks have significant impacts on Filecoin's mining fairness and transaction throughput. For instance, when honest miners who control more than half the global storage power assemble their tipsets after the default transmission cutoff time, an adversary with 1% of the global storage power is able to launch temporary block withholding attacks without a loss in revenue, which is rare in existing blockchains. Finally, we discuss the implications of our attacks and propose several countermeasures to mitigate them.

Fully homomorphic encryption (FHE) is an encryption method that allows to perform computation on encrypted data, without decryption. FHE preserves the privacy of the users of online services that handle sensitive data, such as health data, biometrics, credit scores and other personal information. A common way to provide a valuable service on such data is through machine learning and, at this time, Neural Networks are the dominant machine learning model for unstructured data. In this work we show how to construct Deep Neural Networks (DNN) that are compatible with the constraints of TFHE, an FHE scheme that allows arbitrary depth computation circuits. We discuss the constraints and show the architecture of DNNs for two computer vision tasks. We benchmark the architectures using the Concrete stack, an open-source implementation of TFHE.

GIMPS and PrimeGrid are large-scale distributed projects dedicated to searching giant prime numbers, usually of special forms like Mersenne and Proth. The numbers in the current search-space are millions of digits large and the participating volunteers need to run resource-consuming primality tests. Once a candidate prime $N$ has been found, the only way for another party to independently verify the primality of $N$ used to be by repeating the expensive primality test. To avoid the need for second recomputation of each primality test, these projects have recently adopted certifying mechanisms that enable efficient verification of performed tests. However, the mechanisms presently in place only detect benign errors and there is no guarantee against adversarial behavior: a malicious volunteer can mislead the project to reject a giant prime as being non-prime. In this paper, we propose a practical, cryptographically-sound mechanism for certifying the non-primality of Proth numbers. That is, a volunteer can -- parallel to running the primality test for $N$ -- generate an efficiently verifiable proof at a little extra cost certifying that $N$ is not prime. The interactive protocol has statistical soundness and can be made non-interactive using the Fiat-Shamir heuristic. Our approach is based on a cryptographic primitive called Proof of Exponentiation (PoE) which, for a group $\mathbb{G}$, certifies that a tuple $(x,y,T)\in\mathbb{G}^2\times\mathbb{N}$ satisfies $x^{2^T}=y$ (Pietrzak, ITCS 2019 and Wesolowski, J. Cryptol. 2020). In particular, we show how to adapt Pietrzak's PoE at a moderate additional cost to make it a cryptographically-sound certificate of non-primality.

The development of Artificial Intelligence (AI) based systems to automatically generate hardware systems has gained an impulse that aims to accelerate the hardware design cycle with no human intervention. Recently, the striking AI-based system ChatGPT from OpenAI has achieved a momentous headline and has gone viral within a short span of time since its launch. This chatbot has the capability to interactively communicate with the designers through a prompt to generate software and hardware code, write logic designs, and synthesize designs for implementation on Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuits (ASIC). However, an unvetted ChatGPT prompt by a designer with an aim to generate hardware code may lead to security vulnerabilities in the generated code. In this work, we systematically investigate the necessary strategies to be adopted by a designer to enable ChatGPT to recommend secure hardware code generation. To perform this analysis, we prompt ChatGPT to generate code scenarios listed in Common Vulnerability Enumerations (CWEs) under the hardware design (CWE-1194) view from MITRE. We first demonstrate how a ChatGPT generates insecure code given the diversity of prompts. Finally, we propose techniques to be adopted by a designer to generate secure hardware code. In total, we create secure hardware code for $10$ noteworthy CWEs under hardware design view listed on MITRE site.

This paper proposes Addax, a fast, verifiable, and private online ad exchange. When a user visits an ad-supported site, Addax runs an auction similar to those of leading exchanges; Addax requests bids, selects the winner, collects payment, and displays the ad to the user. A key distinction is that bids in Addax’s auctions are kept private and the outcome of the auction is publicly verifiable. Addax achieves these properties by adding public verifiability to the affine aggregatable encodings in Prio (NSDI’17) and by building an auction protocol out of them. Our implementation of Addax over WAN with hundreds of bidders can run roughly half the auctions per second as a non-private and non-verifiable exchange, while delivering ads to users in under 600 ms with little additional bandwidth requirements. This efficiency makes Addax the first architecture capable of bringing transparency to this otherwise opaque ecosystem.

The production of modern integrated circuit (IC) requires a complex, outsourced supply chain involving computer-aided design (CAD) tools, expert knowledge, and advanced foundries. This complexity has led to various security threats, such as Trojans inserted by adversaries during outsourcing, and physical probing or manipulation of devices at run-time. Our proposed solution, DEFense is an extensible CAD framework for evaluating and proactively mitigating threats to IC at the design-time stage. Our goal with DEFense is to achieve “security closure” at the physical layout level of IC design, prioritizing security alongside traditional power, performance, and area (PPA) objectives. DEFense uses an iterative approach to assess and mitigate vulnerabilities in the IC layout, automating vulnerability assessments and identifying vulnerable active devices and wires. Using the quantified findings, DEFense guides CAD tools to re-arrange placement and routing and use other heuristic means to “DEFend” the layouts. DEFense is independent of back-end CAD tools as it works with the standard DEF format for physical layouts. It is a flexible and extensible scripting framework without the need for modifications to commercial CAD code bases. We are providing the framework to the community and have conducted a thorough experimental investigation into different threats and adversaries at various stages of the IC life-cycle, including Trojan insertion by an untrusted foundry, probing by an untrusted end-user, and intentionally introduced crosstalk by an untrusted foundry.

We study a novel family of cryptographic hash functions based on Galois linear feedback shift registers (LFSRs), and identify initial guidelines for choosing secure parameters for this family. These hash functions are extremely simple, efficient, and suitable for implementation in constrained environments.

Physical side-channel attacks are a major threat to stealing confidential data from devices. There has been a recent surge in such attacks on edge machine learning (ML) hardware to extract the model parameters. Consequently, there has also been some work, although limited, on building corresponding side-channel defenses against such attacks. All the current solutions either take the fully software or fully hardware-centric approaches, which are limited either in performance or flexibility. In this paper, we propose the first hardware-software co-design solution for building side-channel-protected ML hardware. Our solution targets edge devices and addresses both performance and flexibility needs. To that end, we develop a secure RISC-V-based coprocessor design that can execute a neural network implemented in C/C++. The coprocessor uses masking to execute various neural network operations like weighted summations, activation functions, and output layer computation in a side-channel secure fashion. We extend the original RV32I instruction set with custom instructions to control the masking gadgets inside the secure coprocessor. We further use the custom instructions to implement easy-to-use APIs that are exposed to the end-user as a shared library. Finally, we demonstrate the empirical side-channel security of the design with 1M traces.

Proving discrete log equality for groups of the same order is addressed by Chaum and Pedersen's seminal work. However, there has not been a lot of work in proving discrete log equality for groups of different orders. This paper presents an efficient solution, which leverages a technique we call delegated Schnorr. The discovery of this technique is guided by a design methodology that we call the inspection model, and we find it useful for protocol designs. We show two applications of this technique on the Findora blockchain: **Maxwell-Zerocash switching:** There are two privacy-preserving transfer protocols on the Findora blockchain, one follows the Maxwell construction and uses Pedersen commitments over Ristretto, one follows the Zerocash construction and uses Rescue over BLS12-381. We present an efficient protocol to convert assets between these two constructions while preserving the privacy. **Zerocash with secp256k1 keys:** Bitcoin, Ethereum, and many other chains do signatures on secp256k1. There is a strong need for ZK applications to not depend on special curves like Jubjub, but be compatible with secp256k1. Due to FFT unfriendliness of secp256k1, many proof systems (e.g., Groth16, Plonk, FRI) are infeasible. We present a solution using Bulletproofs over curve secq256k1 ("q") and delegated Schnorr which connects Bulletproofs to TurboPlonk over BLS12-381. We conclude the paper with (im)possibility results about Zerocash with only access to a deterministic ECDSA signing oracle, which is the case when working with MetaMask. This result shows the limitations of the techniques in this paper. This paper is under a bug bounty program through a grant from Findora Foundation.

Fully homomorphic encryption (FHE) enables arbitrary computation on encrypted data, allowing users to upload ciphertexts to cloud servers for computation while mitigating privacy risks. Many cryptographic schemes fall under the umbrella of FHE, and each scheme has several open-source implementations with its own strengths and weaknesses. Nevertheless, developers have no straightforward way to choose which FHE scheme and implementation is best suited for their application needs, especially considering that each scheme offers different security, performance, and usability guarantees. To allow programmers to effectively utilize the power of FHE, we employ a series of benchmarks called the Terminator 2 Benchmark Suite and present new insights gained from running these algorithms with a variety of FHE back-ends. Contrary to generic benchmarks that do not take into consideration the inherent challenges of encrypted computation, our methodology is tailored to the secure computational primitives of each target FHE implementation. To ensure fair comparisons, we developed a versatile compiler (called T2) that converts arbitrary benchmarks written in a domain-specific language into identical encrypted programs running on different popular FHE libraries as a backend. Our analysis exposes for the first time the advantages and disadvantages of each FHE library as well as the types of applications most suited for each computational domain (i.e., binary, integer, and floating-point).

We present a single-trace attack against lattice-based KEMs using the cumulative distribution table for Gaussian sampling and execute it in a real-world environment. Our analysis takes a single power trace of the decapsulation algorithm as input and exploits leakage of the Gaussian sampling subroutine to reveal the session key. We investigated the feasibility of the attack on different boards and proved that the power consumption traces become less informative with higher clock frequencies. Therefore, we introduce a machine-learning denoising technique, which enhances the accuracy of our attack and leverages its success rate to 100%. We accomplish the attack on FrodoKEM, a lattice-based KEM and third-round alternate candidate. We execute it on a Cortex-M4 board equipped with an STM32F4 micro-controller clocked at different frequencies.

This paper proposes Prism, Private Verifiable Set Computation over Multi-Owner Outsourced Databases, a secret sharing based approach to compute private set operations (i.e., intersection and union), as well as aggregates over outsourced databases belonging to multiple owners. Prism enables data owners to pre-load the data onto non-colluding servers and exploits the additive and multiplicative properties of secret-shares to compute the above-listed operations in (at most) two rounds of communication between the servers (storing the secret-shares) and the querier, resulting in a very efficient implementation. Also, Prism does not require communication among the servers and supports result verification techniques for each operation to detect malicious adversaries. Experimental results show that Prism scales both in terms of the number of data owners and database sizes, to which prior approaches do not scale.

This article presents and explains methodologies that can be employed to recover information from encrypted files generated by ransomware based on cryptanalytic techniques. By using cryptanalysis and related knowledge as much as possible, the methodology's goal is to use static and dynamic analysis as little as possible. We present three case studies that illustrate different approaches that can be used to recover the encrypted data.

Since the proposal of Bitcoin in 2008, the world has seen accelerated growth in the field of blockchain and discovered its potential to immensely transform most industries, one of the first and most important being finance. The blockchain trilemma states that blockchains can have security, scalability, and decentralization, but never all three at the same time, in the same amount. At the moment, the most successful blockchains have a lack of scalability that researchers and developers try to alleviate by solutions like layer 2s. Most of these solutions rely on cryptographic primitives and technologies, like collision-free hash function or zero-knowledge proofs. In this paper we explore a few of the most popular solutions available now, their improvements to scalability, their drawbacks and security risks.

Back in the 90s when the notion of malware first appeared, it was clear that the behaviour and purpose of such software should be closely analysed, such that systems all over the world should be patched, secured and ready to prevent other malicious activities to be happening in the future. Thus, malware analysis was born. In recent years, the rise of malware of all types, for example trojan, ransowmare, adware, spyware and so on, implies that deeper understanding of operating systems, attention to the details and perseverance are just some of the traits any malware analyst should have in their bag. With Windows being the worldwide go-to operating system, Windows' executable files represent the perfect way in which malware can be disguised to later be loaded and produce damage. In this paper we highlight how ciphers like Vigen\`ere cipher or Caesar cipher can be extended to more complex classes, such that, when later broken, ways of decrypting malware payloads, that are disguised in Windows executable files, are found. Alongside the theoretical information present in this paper, based on a dataset provided by our team at Bitdefender, we describe our implementation on how the key to decryption of such payloads can be found, what techniques are present in our approach, how optimization can be done, what are the pitfalls of this implementation and, lastly, open a discussion on how to tackle these pitfalls.

In practical operational networks, it is essential to validate path integrity, especially when untrusted intermediate nodes are from numerous network infrastructures operated by several network authorities. Current solutions often reveal the entire path to all parties involved, which may potentially expose the network structures to malicious intermediate attackers. Additionally, there is no prior work done to provide a systematic approach combining the complete lifecycle of packet delivery, i.e., path slicing, path validation and path rerouting, leaving these highly-intertwined modules completely separated. In this work, we present a decentralized privacy-preserving path validation system 𝑃3𝑉 that integrates our novel path validation protocol with an efficient path slicing algorithm and a malice-resilient path rerouting mechanism. Specifically, leveraging Non-Interactive Zero-Knowledge proofs, our path validation protocol XOR-Hash-NIZK protects the packet delivery tasks against information leakage about multi-hop paths and potentially the underlying network infrastructures. We implemented and evaluated our system on a state-of-the-art 5G Dispersed Computing Testbed simulating a multi-authority network. Our results show that while preserving the privacy of paths and nodes and enhancing the security of network service, our system optimizes the performance trade-off between network service quality and security/privacy.

Akbarpour and Li (2020) formalized credibility as an auction desideratum where the auctioneer cannot benefit by implementing undetectable deviations from the promised auction and showed that, in the plain model, the ascending price auction with reserves is the only credible, strategyproof, revenue-optimal auction. Ferreira and Weinberg (2020) proposed the Deferred Revelation Auction (DRA) as a communication efficient auction that avoids the uniqueness results from (2020) assuming the existence of cryptographic commitments and as long as bidder valuations are MHR. They also showed DRA is not credible in settings where bidder valuations are $\alpha$-strongly regular unless $\alpha$ > 1. In this paper, we ask if blockchains allow us to design a larger class of credible auctions. We answer this question positively, by showing that DRA is credible even for $\alpha$-strongly regular distributions for all $\alpha$ > 0 if implemented over a secure and censorship-resistant blockchain. We argue ledgers provide two properties that limit deviations from a self-interested auctioneer. First, the existence of smart contracts allows one to extend the concept of credibility to settings where the auctioneer does not have a reputation — one of the main limitations for the definition of credibility from Akbarpour and Li (2020). Second, blockchains allow us to implement mechanisms over a public broadcast channel, removing the adaptive undetectable deviations driving the negative results of Ferreira and Weinberg (2020).

In a single secret leader election protocol (SSLE), one of the system participants is chosen and, unless it decides to reveal itself, no other participant can identify it. SSLE has a great potential in protecting blockchain consensus protocols against denial of service (DoS) attacks. However, all existing solutions either make strong synchrony assumptions or have expiring registration, meaning that they require elected processes to re-register themselves before they can be re-elected again. This, in turn, prohibits the use of these SSLE protocols to elect leaders in partially-synchronous consensus protocols as there may be long periods of network instability when no new blocks are decided and, thus, no new registrations (or re-registrations) are possible. In this paper, we propose Homomorphic Sortition -- the first asynchronous SSLE protocol with non-expiring registration, making it the first solution compatible with partially-synchronous leader-based consensus protocols. Homomorphic Sortition relies on Threshold Fully Homomorphic Encryption (ThFHE) and is tailored to proof-of-stake (PoS) blockchains, with several important optimizations with respect to prior proposals. In particular, unlike most existing SSLE protocols, it works with arbitrary stake distributions and does not require a user with multiple coins to be registered multiple times. Our protocol is highly parallelizable and can be run completely off-chain after setup. Some blockchains require a sequence of rounds to have non-repeating leaders. We define a generalization of SSLE, called Secret Leader Permutation (SLP) in which the application can choose how many non-repeating leaders should be output in a sequence of rounds and we show how Homomorphic Sortition also solves this problem.

As the number of blockchain projects grows, efficient cross-chain interoperability becomes more necessary. A common cross-chain protocol is the two-way peg, which is typically used to transfer assets between blockchains and their sidechains. The criticality of cross-chain protocols require that they are designed with strong security models, which can reduce usability in the form of long transfer times. In this paper, we present Flyover, a repayment protocol to speed up the transfer of bitcoins over federated pegs by allowing untrusted liquidity providers to advance funds for the users. Transfer times are reduced because liquidity providers do not have the same security requirements as the underlying cross-chain protocol. We illustrate the Flyover protocol on the cross-chain interoperability protocol that connects Bitcoin to the RSK sidechain and show how Flyover can reduce transfer times without reducing security. In addition to this, Flyover extends the cross-chain protocol by allowing liquidity providers to make smart contract calls on RSK on behalf of the user.

We propose a single-tiered hybrid Proof-of-Work consensus protocol to encourage decentralization in bitcoin. Our new mechanism comprises coupled puzzles of which properties differ from each other; the one is the extant outsourceable bitcoin puzzle while the other is non-outsourceable. Our new protocol enables miners to solve either puzzle as they want; therefore, blocks can be generated by either puzzle. Our hybrid consensus can be successfully implemented in bitcoin, because it is backward-compatible with existing bitcoin mining equipment(more precisely, existing bitcoin mining ASICs)

We introduce a new notion of {\em multiverse threshold signatures} (MTS). In an MTS scheme, multiple universes -- each defined by a set of (possibly overlapping) signers, their weights, and a specific security threshold -- can co-exist. A universe can be (adaptively) created via a non-interactive asynchronous setup. Crucially, each party in the multiverse holds constant-sized keys and releases compact signatures with size and computation time both independent of the number of universes. Given sufficient partial signatures over a message from the members of a specific universe, an aggregator can produce a short aggregate signature relative to that universe. We construct an MTS scheme building on BLS signatures. Our scheme is practical, and can be used to reduce bandwidth complexity and computational costs in decentralized oracle networks. As an example data point, consider a multiverse containing 2000 nodes and 100 universes (parameters inspired by Chainlink's use in the wild) each of which contains arbitrarily large subsets of nodes and arbitrary thresholds. Each node computes and outputs 1 group element as its partial signature; the aggregator performs under 0.7 seconds of work for each aggregate signature, and the final signature of size 192 bytes takes 6.4 ms (or 198K EVM gas units) to verify. For this setting, prior approaches when used to construct MTS, yield schemes that have one of the following drawbacks: (i) partial signatures that are 97$\times$ larger, (ii) have aggregation times 311$\times$ worse, or (iii) have signature size 39$\times$ and verification gas costs 3.38$\times$ larger. We also provide an open-source implementation and a detailed evaluation.

Designing an efficient solution for Byzantine broadcast is an important problem for many distributed computing and cryptographic tasks. There have been many attempts to achieve sub-quadratic communication complexity in several directions, both in theory and practice, all with pros and cons. This paper initiates the study of another attempt: improving the amortized communication complexity of multi-shot Byzantine broadcast. Namely, we try to improve the average cost when we have sequential multiple broadcast instances. We present a protocol that achieves optimal amortized linear complexity under an honest majority. Our core technique is to efficiently form a network for disseminating the sender's message by keeping track of dishonest behaviors over multiple instances. We also generalize the technique for the dishonest majority to achieve amortized quadratic communication complexity.

In order to propagate transactions and blocks, today’s blockchain systems rely on unstructured peer-to-peer overlay networks. In such networks, broadcast is known to be an inefficient operation in terms of message complexity and overhead. In addition to the impact on the system performance, inefficient or delayed block propagation may have severe consequences regarding security and fairness of the consensus layer. In contrast, the Kadcast protocol is a structured peer-to-peer protocol for block and transaction propagation in blockchain networks. Kadcast utilizes the well-known overlay topology of Kademlia to realize an efficient broadcast operation with tunable overhead. We study the security and privacy of the Kadcast protocol based on probabilistic models and analyze its resilience to packet losses and node failures. Moreover, we evaluate Kadcast’s block delivery performance, broadcast reliability, efficiency, and security based on advanced network simulations. Lastly, we introduce a QUIC-based prototype implementation of the Kadcast protocol and show its merits through deployment in a large-scale cloud-based testbed.

In recent years, many major economies have paid close attention to central bank digital currency (CBDC). As an optional attribute of CBDC, dual offline transaction is considered to have great practical value under the circumstances for payment without network connection. However, there is no public report or paper on how to securely design or implement the dual offline transaction function specifically for CBDC. In this paper, we propose DOT-M, a practical dual offline transaction scheme designed for the mobile device user as either a payer or a payee. Precisely, adopting secure element (SE) and trusted execution environment (TEE), the architecture of trusted mobile device is constructed to protect security-sensitive keys and execution of the transaction protocol. According to the trusted architecture, the data structure for offline transaction is designed as well. On this basis, we describe the core procedures of DOT-M in detail, including registration, account synchronization, dual offline transaction, and online data updating. We also enumerate the exceptional situations that may occur during the dual offline transaction, and give specific handling methods for each situation. Moreover, six security properties of the scheme are analyzed under realistic assumptions. A prototype system is implemented and finally tested with possible parameters. The security analysis and experimental results indicate that our scheme could meet the practical requirement of CBDC offline transaction for mobile users from both aspects of security and efficiency.

The DECentralized Oracle (DECO) protocol enables the verifiable provenance of data from Transport Layer Security (TLS) connections through secure two-party computation and zero-knowledge proofs. In this paper, we present PECO, an extension of DECO that enhances privacy features through the integration of two new private three-party handshake protocols (P3P-HS). PECO allows any web user to prove to a verifier the properties of data from TLS connections without disclosing the identity of the servers. Like DECO's three-party handshake protocol, PECO's P3P-HS methods do not require any changes on the server side. PECO offers two options: one that provides $k-$anonymity for the server's identity, and another that completely masks the server's identity from the verifier. PECO is based on three main protocols: (a) commit-and-proof zero-knowledge proofs (CP-ZKP) that enable the proof of relations under committed values in zero-knowledge, (b) verification of Elliptic Curve Digital Signature Algorithm (ECDSA) signatures under a committed public key without revealing the key (zkAttest), and (c) a proof of membership to verify that a committed key belongs to a set of keys. We estimate the performance of both P3P-HS protocols and compare it to TLS timeout using state-of-the-art implementations.

Password security relies heavily on the choice of password by the user but also on the one-way hash functions used to protect stored passwords. To compensate for the increased computing power of attackers, modern password hash functions like Argon2, have been made more complex in terms of computational power and memory requirements. Nowadays, the computation of such hash functions is performed usually by the server (or authenticator) instead of the client. Therefore, constrained Internet of Things devices cannot use such functions when authenticating users. Additionally, the load of computing such functions may expose servers to denial of service attacks. In this work, we discuss client-side hashing as an alternative. We propose Clipaha, a client-side hashing scheme that allows using high-security password hashing even on highly constrained server devices. Clipaha is robust to a broader range of attacks compared to previous work and covers important and complex usage scenarios. Our evaluation discusses critical aspects involved in client-side hashing. We also provide an implementation of Clipaha in the form of a web library and benchmark the library on different systems to understand its mixed JavaScript and WebAssembly approach's limitations. Benchmarks show that our library is 50\% faster than similar libraries and can run on some devices where previous work fails.

Verifying the absence of maliciously inserted Trojans in ICs is a crucial task – especially for security-enabled products. Depending on the concrete threat model, different techniques can be applied for this purpose. Assuming that the original IC layout is benign and free of backdoors, the primary security threats are usually identified as the outsourced manufacturing and transportation. To ensure the absence of Trojans in commissioned chips, one straightforward solution is to compare the received semiconductor devices to the design files that were initially submitted to the foundry. Clearly, conducting such a comparison requires advanced laboratory equipment and qualified experts. Nevertheless, the fundamental techniques to detect Trojans which require evident changes to the silicon layout are nowadays well-understood. Despite this, there is a glaring lack of public case studies describing the process in its entirety while making the underlying datasets publicly available. In this work, we aim to improve upon this state of the art by presenting a public and open hardware Trojan detection case study based on four different digital ICs using a Red Team vs. Blue Team approach. Hereby, the Red Team creates small changes acting as surrogates for inserted Trojans in the layouts of 90 nm, 65 nm, 40 nm, and 28 nm ICs. The quest of the Blue Team is to detect all differences between digital layout and manufactured device by means of a GDSII–vs–SEM-image comparison. Can the Blue Team perform this task efficiently? Our results spark optimism for the Trojan seekers and answer common questions about the efficiency of such techniques for relevant IC sizes. Further, they allow to draw conclusions about the impact of technology scaling on the detection performance.

This work addresses expressive queries over encrypted data by presenting the first systematic study of multi-attribute range search on a symmetrically encrypted database outsourced to an honest-but-curious server. Prior work includes a thorough analysis of single-attribute range search schemes (e.g. Demertzis et al. 2016) and a proposed high-level approach for multi-attribute schemes (De Capitani di Vimercati et al. 2021). We first introduce a flexible framework for building secure range search schemes over multiple attributes (dimensions) by adapting a broad class of geometric search data structures to operate on encrypted data. Our framework encompasses widely used data structures such as multi-dimensional range trees and quadtrees, and has strong security properties that we formally prove. We then develop six concrete highly parallelizable range search schemes within our framework that offer a sliding scale of efficiency and security tradeoffs to suit the needs of the application. We evaluate our schemes with a formal complexity and security analysis, a prototype implementation, and an experimental evaluation on real-world datasets.

Self-sovereign identity (SSI) systems have gained increasing attention over the last five years. In a variety of fields (e.g., education, IT security, law, government), developers and researchers are attempting to give end-users back their right to and control of their data. Although prototypes and theoretical concepts for SSI applications exist, the majority of them are still in their infancy. Due to missing definitions and standards, there is currently a lack of common understanding of SSI system within the (IT) community. To investigate current commonalities and differences in SSI understanding, I contribute the first qualitative user study (N=13) on expert mental models of SSI and its associated threat landscape. The study results highlight the need for a general definition of SSI and further standards for such systems, as experts' perceptions of SSI requirements vary widely. Based on the expert interviews, I constructed a minimal knowledge map for (potential) SSI end-users and formulated design guidelines for SSI to facilitate broad adoption in the wild and improve privacy-preserving usage.

Publish-subscribe protocols enable real-time multi-point-to-multi-point communications for many dispersed computing systems like Internet of Things (IoT) applications. Recent interest has focused on adding processing to such publish-subscribe protocols to enable computation over real-time streams such that the protocols can provide functionalities such as sensor fusion, compression, and other statistical analysis on raw sensor data. However, unlike pure publish-subscribe protocols, which can be easily deployed with end-to-end transport layer encryption, it is challenging to ensure security in such publish-process-subscribe protocols when the processing is carried out on an untrusted third party. In this work, we present XYZ, a secure publish-process-subscribe system that can preserve the confidentiality of computations and support multi-publisher-multi-subscriber settings. Within XYZ, we design two distinct schemes: the first using Yao's garbled circuits (the GC-Based Scheme) and the second using homomorphic encryption with proxy re-encryption (the Proxy-HE Scheme). We build implementations of the two schemes as an integrated publish-process-subscribe system. We evaluate our system on several functions and also demonstrate real-world applications. The evaluation shows that the GC-Based Scheme can finish most tasks two orders of magnitude times faster than the Proxy-HE Scheme while Proxy-HE can still securely complete tasks within an acceptable time for most functions but with a different security assumption and a simpler system structure.

The advent of quantum computers brought a large interest in post-quantum cryptography and in the migration to quantum-resistant systems. Protocols for Self-Sovereign Identity (SSI) are among the fundamental scenarios touched by this need. The core concept of SSI is to move the control of digital identity from third-party identity providers directly to individuals. This is achieved through Verificable Credentials (VCs) supporting anonymity and selective disclosure. In turn, the implementation of VCs requires cryptographic signature schemes compatible with a proper Zero-Knowledge Proof (ZKP) framework. We describe the two main ZKP VCs schemes based on classical cryptographic assumptions, that is, the signature scheme with efficient protocols of Camenisch and Lysyanskaya, which is based on the strong RSA assumption, and the BBS+ scheme of Boneh, Boyen and Shacham, which is based on the strong Diffie-Hellman assumption. Since these schemes are not quantum-resistant, we select as one of the possible post-quantum alternatives a lattice-based scheme proposed by Jeudy, Roux-Langlois, and Sander, and we try to identify the open problems for achieving VCs suitable for selective disclosure, non-interactive renewal mechanisms, and efficient revocation.

Today, authentication faces the trade-off of security versus usability. Two factor authentication, for example, is one way to improve security at the cost of requiring user interaction for every round of authentication. Most 2FA methods are bound to user's phone and fail if the phone is not available. We propose CoRA, a Collaborative Risk-aware Authentication method that takes advantage of any and many devices that the user owns. CoRA increases security, and preserves usability and privacy by using threshold MACs and by tapping into the knowledge of the devices instead of requiring user knowledge or interaction. Using CoRA, authentication tokens are generated collaboratively by multiple devices owned by the user, and the token is accompanied by a risk factor that indicates the reliability of the token to the authentication server. CoRA relies on a device-centric trust assessment to determine the relative risk factor and on threshold cryptography to ensure no single point of failure. CoRA does not assume any secure element or physical security for the devices. In this paper, we present the architecture and security analysis of CoRA. In an associated user study we discover that 78% of users have at least three devices with them at most times, and 93% have at least two, suggesting that deploying CoRA multi-factor authentication is practical today.

In this work, we introduce a lightweight communication-efficient multi-key approach suitable for the Federated Averaging rule. By combining secret-key RLWE-based HE, additive secret sharing and PRFs, we reduce approximately by a half the communication cost per party when compared to the usual public-key instantiations, while keeping practical homomorphic aggregation performances. Additionally, for LWE-based instantiations, our approach reduces the communication cost per party from quadratic to linear in terms of the lattice dimension.

On the Internet of Connected Vehicles, a vehicle has to communicate bi-directionally with several devices for establishing a shared network for inter-vehicle and intra-vehicle connectivity. These connection protocols are commonly structured to connect all the individual components with an implicit degree of trust, which is supposed to protect the whole system from unauthorized users. Technologies like Automotive Ethernet tend to increase security by reducing the implicit trust within the local network devices. However, the lack of individual security protocols in vehicle-to-vehicle communication still keeps the possession of vulnerability to hacks, external attacks, and further disruption. This is where Zero Trust Architecture can become a reliable technology for the exchange of information in between vehicles. Zero trust is a security system that means no one is trusted by default and verification is required from anyone or any device willing to get connected to the intra-vehicle network. In this paper, we have scoped the preliminary and most vital step of this system: verifying the owner identity of a vehicle with zero trust manner. Our approach involves recognizing vehicle license plates and utilizing the license information for retrieving the vehicle owner details to establish trust before allowing connection to the network. Our proposed methodology operates with 85\% to 99\% accuracy on the license recognition part within recognizable distances using PyTesseract OCR. Reliability to the zero trust solution is gained through necessary information retrieved using GET and POST requests to and from the corresponding driving license information databases.

Digital Identities are playing an essential role in our digital lives. Today, most Digital Identities are based on central architectures. Central Digital Identity providers control and know our data and thereby our Identity. Self Sovereign Identities are based on decentralized data storage and data exchange architecture, where the user is in sole control of his data and identity. Most of the issued credentials need the possibility of revocation. For a centrally managed Digital Identity system, revocation is not a problem. In decentral architectures, revocation is more challenging. Revocation can be done with different methods e.g. list based, cryptographic accumulators and with credential updates. A revocation method must be privacy preserving and must scale. This paper gives an overview of the available revocation methods, including a survey to define requirements, assess revocation groups against the requirements, highlights shortcomings of the methods and introduces a new revocation method called Linked Validity Verifiable Credentials.

The tech industry is currently making the transition from Web 2.0 to Web 3.0, and with this transition, authentication and authorization have been reimag- ined. Users can now sign in to websites with their unique public/private key pair rather than generating a username and password for every site. How- ever, many useful features, like role-based access control, dynamic resource owner privileges, and expiration tokens, currently don’t have efficient Web 3.0 solutions. Our solution aims to provide a flexible foundation for resource providers to implement the aforementioned features on any blockchain through a two-step process. The first step, authorization, creates an on-chain asset which is to be presented as an access token when interacting with a resource. The second step, authentication, verifies ownership of an asset through querying the blockchain and cryptographic digital signatures. Our solution also aims to be a multi-chain standard, whereas current Web 3.0 sign-in standards are limited to a single blockchain.

The time sequence-based outsourcing makes new demands for related access control continue to grow increasingly in cloud computing. In this paper, we propose a practical password-based access control framework for such media cloudization relying on content control based on the time-sequence attribute, which is designed over prime-order groups. First, the scheme supports multi-keyword search simultaneously in any monotonic boolean formulas, and enables media owner to control content encryption key for different time-periods using an updatable password; Second, the scheme supports the key self-retrievability of content encryption key, which is more suitable for the cloud-based media applications with massive users. Then, we show that the proposed scheme is provably secure in the standard model. Finally, the detailed result of performance evaluation shows the proposed scheme is efficient and practical for cloud-based media applications.

Adopting Post-Quantum Cryptography (PQC) in network protocols is a challenging subject. Larger PQC public keys and signatures can significantly slow the Transport Layer Security (TLS) protocol. In this context, KEMTLS is a promising approach that replaces the handshake signatures by using PQC Key Encapsulation Mechanisms (KEMs), which have, in general, smaller sizes. However, for broad PQC adoption, hybrid cryptography has its advantages over PQC-only approaches, mainly about the confidence in the security of existing cryptographic schemes. This work brings hybrid cryptography to the KEMTLS and KEMTLS-PDK protocols. We analyze different network conditions and show that the penalty when using Hybrid KEMTLS over PQC-only KEMTLS is minor under certain security levels. We also compare Hybrid KEMTLS with a hybrid version of PQTLS. Overall, the benefits of using hybrid protocols outweigh the slowdown penalties in higher security parameters, which encourages its use in practice.

We introduce a new way to conduct election audits using untrusted scanners. Post-election audits perform statistical hypothesis testing to confirm election outcomes. However, existing approaches are costly and laborious for close elections---often the most important cases to audit---requiring extensive hand inspection of ballots. We instead propose automated consistency checks, augmented by manual checks of only a small number of ballots. Our protocols scan each ballot twice, shuffling the ballots between scans: a ``two-scan'' approach inspired by two-prover proof systems. We show that this gives strong statistical guarantees even for close elections, provided that (1) the permutation accomplished by the shuffle is unknown to the scanners and (2) the scanners cannot reliably identify a particular ballot among others cast for the same candidate. Our techniques drastically reduce the time, expense, and labor of auditing close elections, which we hope will promote wider deployment. We present three rescan audit protocols and analyze their statistical guarantees. We first present a simple scheme illustrating our basic idea in a simplified two-candidate setting. We then extend this scheme to support (1) more than two candidates; (2) processing of ballots in batches; and (3) imperfect scanners, as long as scanning errors are too infrequent to affect the election outcome. Our proposals require manual handling or inspection of 10--100 ballots per batch in a variety of settings, in contrast to existing techniques that require hand inspecting many more ballots in close elections. Unlike prior techniques that depend on the relative margin of victory, our protocols are to our knowledge the first to depend on the absolute margin, and give meaningful guarantees even for extremely close elections: e.g., absolute margins of tens or hundreds of votes.

Private comparison schemes constructed on homomorphic encryption offer the noninteractive, output expressive and parallelizable features, and have advantages in communication bandwidth and performance. In this paper, we propose cuXCMP, which allows negative and float inputs, offers fully output expressive feature, and is more extensible and practical compared to XCMP (AsiaCCS 2018). Meanwhile, we introduce several memory-centric optimizations of the constant term extraction kernel tailored for CUDA-enabled GPUs. Firstly, we fully utilize the shared memory and present compact GPU implementations of NTT and INTT using a single block; Secondly, we fuse multiple kernels into one AKS kernel, which conducts the automorphism and key switching operation, and reduce the grid dimension for better resource usage, data access rate and synchronization. Thirdly, we precisely measure the IO latency and choose an appropriate number of CUDA streams to enable concurrent execution of independent operations, yielding a constant term extraction kernel with perfect latency hide, i.e., CTX. Combining these approaches, we boost the overall execution time to optimum level and the speedup ratio increases with the comparison scales. For one comparison, we speedup the AKS by 23.71×, CTX by 15.58×, and scheme by 1.83× (resp., 18.29×, 11.75×, and 1.42×) compared to C (resp., AVX512) baselines, respectively. For 32 comparisons, our CTX and scheme implementations outperform the C (resp., AVX512) baselines by 112.00× and 1.99× (resp., 81.53× and 1.51×).

For avoding the attacks from quantum computing (QC), this study applies the post-quantum cryptography (PQC) methods without hidden subgroups to the security of vehicular communications. Due the mainstream technologies of PQC methods (i.e. lattice-based cryptography methods), the standard digital signature methods including Dilithium and Falcon have been discussed and compared. This study uses a queueing model to analyze the performance of these standard digital signature methods for selection decision-making.

Sharing a secret efficiently amongst a group of participants is not easy since there is always an adversary / eavesdropper trying to retrieve the secret. In secret sharing schemes, every participant is given a unique share. When the desired group of participants come together and provide their shares, the secret is obtained. For other combinations of shares, a garbage value is returned. A threshold secret sharing scheme was proposed by Shamir and Blakeley independently. In this (n,t) threshold secret sharing scheme, the secret can be obtained when at least $t$ out of $n$ participants contribute their shares. This paper proposes a novel algorithm to reveal the secret only to the subsets of participants belonging to the access structure. This scheme implements totally generalized ideal secret sharing. Unlike threshold secret sharing schemes, this scheme reveals the secret only to the authorized sets of participants, not any arbitrary set of users with cardinality more than or equal to $t$. Since any access structure can be realized with this scheme, this scheme can be exploited to implement various access priorities and access control mechanisms. A major advantage of this scheme over the existing ones is that the shares being distributed to the participants is totally independent of the secret being shared. Hence, no restrictions are imposed on the scheme and it finds a wider use in real world applications.

The recent advent of cloud computing and IoT has made it imperative to store huge amount of data in the cloud servers. Enormous amount of data is also stored in the servers of big organizations. In organizations, it is not desirable for every member to have equal privileges to access the stored data. Threshold secret sharing schemes are widely used for implementing such access control mechanisms. The access privileges of the members also vary from one data packet to another. While implementing such dynamic access structures using threshold secret sharing schemes, the key management becomes too complex to be implemented in real time. Furthermore, the storage of the users’ access privileges requires exponential space (O($2^n$)) and its implementation requires exponential time. In this paper, the algorithms proposed to tackle the problems of priority based access control and authentication require a space complexity of O($n^2$) and a time complexity of O($n$), where n is the number of users. In the practical scenario, such space can easily be provided on the servers and the algorithms can run smoothly in real time.

Off-chain payment channels were introduced as one of the solutions to the blockchain scalability problem. The channels shape a network, where parties have to lock funds for their creation. A channel is expected to route a limited number of transactions before it becomes unbalanced, when all of the funds are devoted to one of the parties. Since an on-chain transaction is often necessary to establish, rebalance, or close a channel, the off-chain network is bounded to the throughput of the blockchain. In this paper, we propose a mathematical model to formulate limitation on the throughput of an off-chain payment network. As a case study, we show the limitation of the Lightning Network, in comparison with popular banking systems. Our results show that theoretically, the throughput of the Lightning Network can reach the order of 10000 transactions per second, close to the average throughput of centralized banking systems.

Additively Homomorphic Encryption (AHE) has been widely used in various applications, such as federated learning, blockchain, and online auctions. Elliptic Curve (EC) based AHE has the advantages of efficient encryption, homomorphic addition, scalar multiplication algorithms, and short ciphertext length. However, EC-based AHE schemes require solving a small exponential Elliptic Curve Discrete Logarithm Problem (ECDLP) when running the decryption algorithm, i.e., recovering the plaintext $m\in\{0,1\}^\ell$ from $m \ast G$. Therefore, the decryption of EC-based AHE schemes is inefficient when the plaintext length $\ell > 32$. This leads to people being more inclined to use RSA-based AHE schemes rather than EC-based ones. This paper proposes an efficient algorithm called $\mathsf{FastECDLP}$ for solving the small exponential ECDLP at $128$-bit security level. We perform a series of deep optimizations from two points: computation and memory overhead. These optimizations ensure efficient decryption when the plaintext length $\ell$ is as long as possible in practice. Moreover, we also provide a concrete implementation and apply $\mathsf{FastECDLP}$ to some specific applications. Experimental results show that $\mathsf{FastECDLP}$ is far faster than the previous works. For example, the decryption can be done in $0.35$ ms with a single thread when $\ell = 40$, which is about $30$ times faster than that of Paillier. Furthermore, we experiment with $\ell$ from $32$ to $54$, and the existing works generally only consider $\ell \leq 32$. The decryption only requires $1$ second with $16$ threads when $\ell = 54$. In the practical applications, we can speed up model training of existing vertical federated learning frameworks by $4$ to $14$ times. At the same time, the decryption efficiency is accelerated by about $140$ times in a blockchain financial system (ESORICS 2021) with the same memory overhead.

Using a novel circuit design, we investigate if the modeling-resistance of delay-based, CMOS-compatible strong PUFs can be increased by the usage of multiple delay lines. Studying a circuit inspired by the Arbiter PUF, but using four instead of merely two delay lines, we obtain evidence showing that the usage of many delay lines does not significantly increase the security of the strong PUF circuit. Based on our findings, we suggest future research directions.

The existing works on privacy-preserving recommender systems based on homomorphic encryption do not filter top-k most relevant items on the server side. As a result, sending the encrypted rating vector for all items to the user retrieving the top-k items is necessary. This incurs significant computation and communication costs on the user side. In this work, we employ private sorting at the server to reduce the user-side overheads. In private sorting, the values and corresponding positions of elements must remain private. We use an existing private sorting protocol by Foteini and Olga and tailor it to the privacy-preserving top-k recommendation applications. We enhance it to use secure bit decomposition in the private comparison routine of the protocol. This leads to a notable reduction in cost overheads of users as well as the servers, especially at the keyserver where the computation cost is reduced to half. The dataserver does not have to perform costly encryption and decryption operations. It performs computationally less expensive modular exponentiation operations. Since the private comparison operation contributes significantly to the overall cost overhead, making it efficient enhances the sorting protocol’s performance. Our security analysis concludes that the proposed scheme is as secure as the original protocol.

While online interactions and exchanges have grown exponentially over the past decade, most commercial infrastructures still operate through centralized protocols, and their success essentially depends on trust between different economic actors. Digital advances such as blockchain technology has led to a massive wave of Decentralized Ledger Technology (DLT) initiatives, protocols and solutions. This advance makes it possible to implement trustless systems in the real world, which, combined with appropriate economic and participatory incentives, would foster the proper functioning and drive the adoption of a decentralized platform among different actors. This paper describes an alternative to current commercial structures and networks by introducing Lyzis Labs, which is is an incentive-driven and democratic protocol designed to support a decentralized online marketplace, based on blockchain technology. The proposal, Lyzis Marketplace, allows to connect two or more people in a decentralized and secure way without having to rely on a Trusted Third Party (TTP) in order to perform physical asset exchanges while mainly providing transparent and fully protected data storage. This approach can potentially lead to the creation of a permissionless, efficient, secure and transparent business environment where each user can gain purchasing and decision-making power by supporting the collective welfare while following their personal interests during their various interactions on the network.

We focus on the problem of efficiently deploying a federated learning training task in a decentralized setting with multiple aggregators. To that end, we introduce a number of improvements and modifications to the recently proposed IPLS protocol. In particular, we relax its assumption for direct communication across participants, using instead indirect communication over a decentralized storage system, effectively turning it into a partially asynchronous protocol. Moreover, we secure it against malicious aggregators (that drop or alter data) by relying on homomorphic cryptographic commitments for efficient verification of aggregation. We implement the modified IPLS protocol and report on its performance and potential bottlenecks. Finally, we identify important next steps for this line of research.

Permissionless blockchains are too slow for applications like point-of-sale payments. While several techniques have been proposed to speed up blockchain payments, none of them are satisfactory for application scenarios like retail shopping. In particular, existing solutions like payment channels require users to lock up significant funds and schemes based on pre-defined validators enable easy transaction censoring. In this paper, we develop Quicksilver, the first blockchain payment scheme that works with practical collaterals and is fast, censorship-resilient, and confidential at the same time.We implement Quicksilver for EVM-compatible chains and show that censoring-resilient payments are fast and affordable on currently popular blockchains platforms like Ethereum and Polygon.

With the rapid growth of the blockchain market, privacy and security issues for digital assets are becoming more important. In the most widely used public blockchains such as Bitcoin and Ethereum, all activities on user accounts are publicly disclosed, which violates privacy regulations such as EU GDPR. Encryption of accounts and transactions may protect privacy, but it also raises issues of validity and transparency: encrypted information alone cannot verify the validity of a transaction and makes it difficult to meet anti-money laundering regulations, i.e. auditability. In this paper, we propose $\textsf{Azeroth}$, an auditable zero-knowledge transfer framework. $\textsf{Azeroth}$ connects a zero-knowledge proof to an encrypted transaction, enabling it to check its validation while protecting its privacy. $\textsf{Azeroth}$ also allows authorized auditors to audit transactions. $\textsf{Azeroth}$ is designed as a smart contract for flexible deployment on existing blockchains. %According to the result of our experiment, the proof generation time is about $0.9s$, and the asset transferring time is only $4.4s$, which is practically usable. We implement the $\textsf{Azeroth}$ smart contract, execute it on various platforms including an Ethereum testnet blockchain, and measure the time to show the practicality of our proposal. The end-to-end latency of a privacy-preserving transfer takes about $4.4s$. In particular, the client's transaction generation time with a proof only takes about $0.9s$. The security of $\textsf{Azeroth}$ is proven under the cryptographic assumptions.

Blockchain is a disruptive technology that promises a multitude of benefits such as transparency, traceability, and immutability. However, this unique bundle of key characteristics rapidly proved to be a double-edged sword that can put user privacy at risk. Unlike traditional systems, Bitcoin transactions are publicly and permanently recorded, and anyone can access the full history of the records. Despite using pseudonymous identities, an adversary can undermine the financial privacy of users and reveal their actual identities using advanced heuristics and techniques to identify eventual links between transactions, senders, receivers, and consumed services (e.g., online purchases). In this regard, a multitude of approaches has been proposed in an attempt to overcome financial transparency and enhance user anonymity. These techniques range from using mixing services to off-chain transactions and address different privacy issues. In this survey, we particularly focus on comparing and evaluating mixing techniques in the Bitcoin blockchain, present their limitations, and highlight the new challenges.

End-to-end encryption provides strong privacy protections to billions of people, but it also complicates efforts to moderate content that can seriously harm people. To address this concern, Tyagi et al. [CRYPTO 2019] introduced the concept of asymmetric message franking (AMF), which allows people to report abusive content to a moderator, while otherwise retaining end-to-end privacy by default and even compatibility with anonymous communication systems like Signal’s sealed sender. In this work, we provide a new construction for asymmetric message franking called Hecate that is faster, more secure, and introduces additional functionality compared to Tyagi et al. First, our construction uses fewer invocations of standardized crypto primitives and operates in the plain model. Second, on top of AMF’s accountability and deniability requirements, we also add forward and backward secrecy. Third, we combine AMF with source tracing, another approach to content moderation that has previously been considered only in the setting of non-anonymous networks. Source tracing allows for messages to be forwarded, and a report only identifies the original source who created a message. To provide anonymity for senders and forwarders, we introduce a model of "AMF with preprocessing" whereby every client authenticates with the moderator out-of-band to receive a token that they later consume when sending a message anonymously.

There are a multitude of Blockchain-based physical infrastructure systems, ranging from decentralized 5G wireless to electric vehicle charging networks. These systems operate on a crypto-currency enabled token economy, where node suppliers are rewarded with tokens for enabling, validating, managing and/or securing the system. However, today's token economies are largely designed without infrastructure systems in mind, and often operate with a fixed token supply (e.g., Bitcoin). Such fixed supply systems often encourage early adopters to hoard valuable tokens, thereby resulting in reduced incentives for new nodes when joining or maintaining the network. This paper argues that token economies for infrastructure networks should be structured differently - they should continually incentivize new suppliers to join the network to provide services and support to the ecosystem. As such, the associated token rewards should gracefully scale with the size of the decentralized system, but should be carefully balanced with consumer demand to manage inflation and be designed to ultimately reach an equilibrium. To achieve such an equilibrium, the decentralized token economy should be adaptable and controllable so that it maximizes the total utility of all users, such as achieving stable (overall non-inflationary) token economies. Our main contribution is to model infrastructure token economies as dynamical systems - the circulating token supply, price, and consumer demand change as a function of the payment to nodes and costs to consumers for infrastructure services. Crucially, this dynamical systems view enables us to leverage tools from mathematical control theory to optimize the overall decentralized network’s performance. Moreover, our model extends easily to a Stackelberg game between the controller and the nodes, which we use for robust, strategic pricing. In short, we develop predictive, optimization-based controllers that outperform traditional algorithmic stablecoin heuristics by up to $2.4 \times$ in simulations based on real demand data from existing decentralized wireless networks.

Quantum computing threatens classical cryptography, leading to the search for stronger alternatives. The cryptographic approach based on lattices is considered as a viable option. Schemes with that approach use Gaussian sampling, a design which brings along two concerns: efficiency and information leakage. This work addresses those concerns in the RLWE formulation, for digital signatures. Efficiency mitigation uses the central limit theorem, and the Walsh–Hadamard transform, whereas the information leakage risk is reduced via isochronous implementation. Up to \( 2^{23} \) samples are queried, and the results are compared against those of a cumulative distribution table sampler. Statistical metrics show the suitability of the presented sampler in a number of contexts.

Fault injection attacks (FIA) are a class of active physical attacks, mostly used for malicious purposes such as extraction of cryptographic keys, privilege escalation, attacks on neural network implementations. There are many techniques that can be used to cause the faults in integrated circuits, many of them coming from the area of failure analysis. In this paper we tackle the topic of practicality of FIA. We analyze the most commonly used techniques that can be found in the literature, such as voltage/clock glitching, electromagnetic pulses, lasers, and Rowhammer attacks. To summarize, FIA can be mounted on most commonly used architectures from ARM, Intel, AMD, by utilizing injection devices that are often below the thousand dollar mark. Therefore, we believe these attacks can be considered practical in many scenarios, especially when the attacker can physically access the target device.

A fault attack (FA) is one of the most potent threats to cryptographic applications. Implementing a FA-protected block cipher requires knowledge of the exploitable fault space of the underlying crypto algorithm. The discovery of exploitable faults is a challenging problem that demands human expertise and time. Current practice is to rely on certain predefined fault models. However, the applicability of such fault models varies among ciphers. Prior work discovers such exploitable fault models individually for each cipher at the expanse of a large amount of human effort. Our work completely replaces human effort by using reinforcement learning (RL) over the huge fault space of a block cipher to discover the effective fault models automatically. Validation on an AES block cipher demonstrates that our approach can automatically discover the effective fault models within a few hours, outperforming prior work, which requires days of manual analysis. The proposed approach also reveals vulnerabilities in the existing FA-protected block ciphers and initiates an end-to-end vulnerability assessment flow.

Machine learning as a service (MLaaS) has risen to become a prominent technology due to the large development time, amount of data, hardware costs, and level of expertise required to develop a machine learning model. However, privacy concerns prevent the adoption of MLaaS for applications with sensitive data. A promising privacy preserving solution is to use fully homomorphic encryption (FHE) to perform the ML computations. Recent advancements have lowered computational costs by several orders of magnitude, opening doors for secure practical applications to be developed. In this work we introduce the REDsec framework that optimizes FHE-based private machine learning inference by leveraging ternary neural networks. Such neural networks, whose weights are constrained to {-1,0,1}, have special properties that we exploit to operate efficiently in the homomorphic domain. REDsec introduces novel features, including a new data re-use scheme that enables bidirectional bridging between the integer and binary domains for the first time in FHE. This enables us to implement very efficient binary operations for multiplication and activations, as well as efficient integer domain additions. Our approach is complemented by a new GPU acceleration library, dubbed (RED)cuFHE, which supports both binary and integer operations on multiple GPUs. REDsec brings unique benefits by supporting user-defined models as input (bring-your-own-network), automation of plaintext training, and efficient evaluation of private inference leveraging TFHE. In our analysis, we perform inference experiments with the MNIST, CIFAR-10, and ImageNet datasets and report performance improvements compared to related works.

Users of decentralized financial networks suffer from inventive security exploits. Identity-based fraud prevention methods are inapplicable in these networks, as they contradict their privacy-minded design philosophy. Novel mitigation strategies are therefore needed. Their rollout, however, may damage other desirable network properties. In this work, we introduce an evaluation framework for mitigation strategies in decentralized financial networks. This framework allows researchers and developers to examine and compare proposed protocol modifications along multiple axes, such as privacy, security, and user experience. As an example, we focus on the jamming attack in the Lightning Network. Lightning is a peer-to-peer payment channel network on top of Bitcoin. Jamming is a cheap denial-of-service attack that allows an adversary to temporarily disable Lightning channels by flooding them with failing payments. We propose a practical solution to jamming that combines unconditional fees and peer reputation. Guided by the framework, we show that, while discouraging jamming, our solution keeps the protocol incentive compatible. It also preserves security, privacy, and user experience, and is straightforward to implement. We support our claims analytically and with simulations. Moreover, our anti-jamming solution may help alleviate other Lightning issues, such as malicious channel balance probing.

White-box cryptography has been proposed as a software protection technique for applications where limited or no hardware-based security is available. In recent years it has been crucial for enabling the security of mobile payment applications. In this paper we continue a recent line of research on device binding for white-box cryptography. Device binding ensures that a white-box program is only executable on one specific device and is unusable elsewhere. Building on this, we ask the following question: is it possible to design a {\em global} white-box program which is compiled once, but can be securely shared with multiple users and bound to each of their devices? Acknowledging this question, we define different flavours of security for such global white-boxes and provide corresponding constructions. We first consider families of \emph{strong} global white-boxes which can be securely distributed and bound to users' devices without the need of sharing secrets between the compiling entity and the users. We then show how such strong global white-boxes can be constructed based on message recoverable signatures (MRS). To this end, we introduce \emph{puncturable} MRS which we build based on puncturable PRFs and indistinguishability obfuscation. Later in the paper we consider the use of Token-Based Obfuscation (TBO) and show that TBO can provide us a direct way to construct global white-boxes, as long as we can securely share a token generation key between the users and compiling entities. While such global white-boxes have weaker security guarantees than their stronger counterparts, our results show that white-box crypto can be built from more accepted assumptions than previously considered. Moreover, TBO allows us to construct white-boxes for any functionality or encryption scheme and thus provides us a very general feasibility result for device binding.