Mohammed Alghazwi, Tariq Bontekoe, Leon Visscher, Fatih TĂŒrkmen
Abstract Non-interactive zero-knowledge (NIZK) proofs of knowledge have proven to be highly relevant for securely realizing a wide array of applications that rely on both privacy and correctness . They enable a prover to convince any party of the correctness of a public statement for a secret witness . However, most NIZKs do not natively support proving knowledge of a secret witness that is distributed over multiple provers. Previously, collaborative proofs [54] have been proposed to overcome this limitation. We investigate the notion of composability in this setting, following the Commit-and-Prove design of LegoSNARK [19]. Composability allows users to combine different, specialized NIZKs (e.g., one for arithmetic circuits, one for boolean circuits, and one for range proofs) with the aim of reducing the proof generation time. Moreover, it opens the door to efficient realizations of many applications in the collaborative setting such as mutually exclusive prover groups, combining collaborative and single-party proofs and efficiently implementing publicly auditable secure multiparty computing (PA-MPC). We present the first, general definition for collaborative commitand- prove NIZK (CP-NIZK) proofs of knowledge and construct MPC protocols to enable their realization. We implement our protocols for two commonly used NIZKs, Groth16 and Bulletproofs, and evaluate their practicality in a variety of computational settings. Our findings indicate that composability adds only minor overhead, especially for large circuits. We also evaluated our construction in two application settings, one of which shows 18â $$55\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>55</mml:mn> <mml:mo>Ă</mml:mo> </mml:mrow> </mml:math> runtime reduction when compared to prior works while requiring only a fraction ( $$0.2\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.2</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ) of the communication.
Maria InĂȘs Silva, Johnnatan Messias, Benjamin Livshits
Despite blockchain data being publicly available, practical challenges and high costs often hinder its effective use by researchers, thus limiting data-driven research and exploration in the blockchain space. This is especially true when it comes to Layer-2 (L2) ecosystems, and ZKsync, in particular. To address these issues, we have curated a dataset from 1 year of activity extracted from a ZKsync Era archive node and made it freely available to external parties. We provide details on this dataset and how it was created, showcase a few example analyses that can be performed with it, and discuss some future research directions.
Domenico Tortola, Andrea Lisi, Paolo Mori, Laura Ricci
A blockchain is a data structure consisting of a list of blocks containing transactions and maintained by a network of nodes in a decentralized manner. In permissionless blockchains, anyone can contribute to the decentralization and security of the transactions. With the advent of smart contracts, programs whose execution is replicated by all the nodes of the network, the blockchain can be deemed not only a reliable and auditable data repository, but also a secure and verifiable computational infrastructure. However, due to the aforementioned features, the throughput of most permissionless blockchains is low, and executing a smart contract can be expensive, depending on its computational complexity. To mitigate these issues, a popular research line studies the implementation of Layer 2 solutions, which consists of nodes that operate off-chain yet remaining tethered to the blockchain. Our literature analysis revealed that a majority of the research articles surveying Layer 2 technologies and solutions typically classify them on the basis of the Layer 2 operations they perform, as well as their ability to improve the processing capacity of the blockchain. In this paper, instead, we survey the methodologies that provide a secure binding between Layer 2 and the blockchain. We refer to these binding techniques as âproving schemesâ which we classify as: data integrity proofs, validity proofs, and fraud proofs. For each proving scheme, we describe its intended purpose, the advantages it offers, the methodologies commonly used to connect the operations performed at Layer 2 with the blockchain, and the applications that benefit from such scheme. Finally, we discuss and compare them to give a general comprehension about how schemes can satisfy general requirements common to most Decentralized Applications.
Modern cryptography relies heavily on the principles of algebraic structures to ensure the security and integrity of data. This paper explores the fundamental algebraic structures that underpin contemporary cryptographic systems, including groups, rings, fields, and lattices. We provide a detailed examination of how these structures are employed in various cryptographic algorithms and protocols, such as public-key cryptography, digital signatures, and hash functions. an overview of basic algebraic concepts and their properties, followed by an in-depth analysis of their applications in cryptographic schemes. For instance, the use of elliptic curve groups in Elliptic Curve Cryptography (ECC) offers enhanced security with smaller key sizes compared to traditional systems like RSA. Similarly, lattice-based cryptography presents promising solutions for post-quantum security, leveraging the hardness of lattice problems to resist attacks by quantum computers. the role of algebraic structures in the development of advanced cryptographic techniques, such as homomorphic encryption, which allows computations on encrypted data without decryption, and zero-knowledge proofs, which enable the verification of information without revealing the information itself. Through these examples, we illustrate the critical importance of algebraic structures in achieving robust and efficient cryptographic systems.
Jingcheng Zhang, Yingxuan Ren, Man Ho Au, Ka-Ho Chow · 9 authors
Abstract With the rapid developments in sequencing technologies, individuals now have unprecedented access to their genomic data. However, existing data management systems or protocols are inadequate for protecting privacy, limiting individualsâ control over their genomic information, hindering data sharing, and posing a challenge for biomedical research. To fill the gap, an owner-governed system that fulfills owner authority, lifecycle data encryption, and verifiability at the same time is prompted. In this paper, we realized Governome, an owner-governed data management system designed to empower individuals with absolute control over their genomic data during data sharing. Governome uses a blockchain to manage all transactions and permissions, enabling data owners with dynamic permission management and to be fully informed about every data usage. It uses homomorphic encryption and zero-knowledge proofs to enable genomic data storage and computation in an encrypted and verifiable form for its whole lifecycle. Governome supports genomic analysis tasks, including individual variant query, cohort study, GWAS analysis, and forensics. Query of a variantâs genotype distribution among 2,504 1kGP individuals in Governome can be efficiently completed in under 18 hours on an ordinary server. Governome is an open-source project available at https://github.com/HKU-BAL/Governome .
Abstract Suppose that a sequence of $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> cards, numbered 1 to $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> , is placed face up in random order. Let $${\varvec{k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> </mml:math> be the number on the first card in the sequence. Then take the first $${\varvec{k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> </mml:math> cards from the sequence, rearrange that subsequence of $${\varvec{k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> </mml:math> cards in reverse order, and return them to the original sequence. Repeat this prefix reversal until the number on the first card in the sequence becomes 1. This is a one-player card game called Topswops. The computational complexity of Topswops has not been thoroughly investigated. For example, letting $${\varvec{f}}({\varvec{n}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> denote the maximum number of prefix reversals for Topswops with $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> cards, values of $${\varvec{f}}({\varvec{n}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> for $${\varvec{n}}\ge 20$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>â„</mml:mo> <mml:mn>20</mml:mn> </mml:mrow> </mml:math> remain unknown. In general, there is no known efficient algorithm for finding an initial sequence of $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> cards that requires exactly $$\ell $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>â</mml:mi> </mml:math> prefix reversals for any integers $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> and $${\varvec{\ell }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>â</mml:mi> </mml:mrow> </mml:math> . In this paper, using a deck of cards, we propose a physical zero-knowledge proof protocol that allows a prover to convince a verifier that the prover knows an initial sequence of $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> cards that requires $${\varvec{\ell }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>â</mml:mi> </mml:mrow> </mml:math> prefix reversals without leaking knowledge of that sequence. We also deal with Botdrops, a variant of Topswops.
Blockchain full nodes are pivotal for transaction availability, as they store the entire ledger, but verifying their storage integrity faces challenges from malicious remote storage attacks such as Sybil, outsourcing, and generation attacks. However, there is no suitable proof-of-storage solution for blockchain full nodes to ensure a healthy number of replicas of the ledger. Existing proof-of-storage solutions are designed for general-purpose settings where a data owner uses secret information to verify storage, rendering them unsuitable for blockchain where proof-of-storage must be fast, publicly verifiable, and data owner-agnostic. This paper introduces a decentralised and quantum-resistant solution named Non-interactive Practical Proof of Storage (nPPoS) with an asymmetric encoding and decoding scheme, for fast and secure PoStorage, and Zero-Knowledge Scalable Transparent Arguments of Knowledge (zk-STARKs), for public variability in blockchain full nodes. The algorithm with asymmetric times for encoding and decoding creates unique block replicas and corresponding proofs for each storage node to mitigate malicious remote attacks and minimise performance degradation. The intentional resource-intensive encoding deters attacks, while faster decoding minimises performance overhead. Through zk-STARKs, nPPoS achieves public verifiability enabling one-to-many verification for scalability, quantum resistance and decentralisation. It also introduces a two-phase randomisation technique and a time-weighted trustworthiness measurement for scalability and adaptability.
Abstract In response to the dual privacy protection challenges concerning the confidentiality of transaction amounts and identities in crossâborder trade, a transaction scheme that combines + HomEIG Zero Knowledge Proof ( + HomEIGâZKProof) and the national encryption algorithm SM2 is proposed. While ensuring transaction traceability and verifiability, this scheme achieves privacy protection for both payersâ and recipientsâ identities, specifically tailored for crossâborder trade scenarios. Additionally, customs authorities play the role of supervisory nodes to verify the identities of transaction parties and the zeroâknowledge proofs for transaction information. The RAFT consensus algorithm is employed to construct a secure authentication application, demonstrating how zeroâknowledge proofs, combined with homomorphic encryption, can be verified through a consensus process. In this scenario, the legitimacy of transaction amounts is subject to zeroâknowledge verification during consensus interactions. Merchant identity verification is accomplished using SM2 ring signatures. The analysis indicates that this scheme offers strong security features such as resistance to tampering attacks, public key replacement attacks, impersonation attacks, and anonymity. Testing results demonstrate that this scheme can effectively provide dual privacy protection for transaction amounts and identities in crossâborder trade, meeting the practical requirements of privacy protection in crossâborder trade transactions.
Abstract A zero-knowledge proof is a cryptographic primitive that enables a prover to convince a verifier the validity of a mathematical statement (an NP statement) without revealing any secret inputs to the verifier. A special case, called zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK) is particularly designed for arithmetic circuit proof systems which have important applications in blockchain privacy. The major computations in this type of zkSNARK proofs with post-quantum security are polynomial evaluations and Lagrange interpolations over finite fields. Given a sequence over a finite field, in the field of coding and sequences research, we understand that there are two representations of the sequence, one is a univariate polynomial and the other, a multivariate polynomial. This is exactly what is done in those zero-knowledge proof systems to transform the proof of a R1CS relation to evaluate uni/multi variate polynomials at some random points in the finite field. In this paper, we present a comparative analysis on how to convert a rank 1 constrained satisfiability (R1CS) system (more general than a circuit system) into a polynomial equality and provide analysis on the concrete complexities of provers, proof sizes and verifiers. We use two concrete zkSNARK schemes, i.e., Polaris, univariate polynomial encodings and Spartan, multivariate polynomial encodings, as examples to show our analysis. Secondly, we propose to select interpolating sets as subfields instead of affine spaces of a large field for Lagrange interpolation. This new method has improved the performance of R1CS encodings largely. We comment that post-quantum secure zkSNARKs yield post-quantum digital signatures with security only depending on symmetric-key schemes. Some open problems are proposed at the end of the paper.
Threshold cryptography is essential for many blockchain protocols. For example, many protocols rely on threshold common coin to implement asynchronous consensus, leader elections, and provide support for randomized applications. Similarly, threshold decryption and threshold time-lock puzzles are often necessary for privacy. In this paper, we study the interplay between threshold cryptography and a class of blockchains that use Byzantine-fault tolerant (BFT) consensus protocols with a focus on latency. More specifically, we focus on blockchain-native threshold cryptosystem, where the blockchain validators seek to run a threshold cryptographic protocol once for every block with the block contents as an input to the threshold cryptographic protocol. All existing approaches for blockchain-native threshold cryptosystems introduce a latency overhead of at least one message delay for running the threshold cryptographic protocol. In this paper, we first propose a mechanism to eliminate this overhead for blockchain-native threshold cryptosystems with tight thresholds, i.e., in threshold cryptographic protocols where the secrecy and reconstruction thresholds are the same. However, many real-world proof-of-stake-based blockchain-native threshold cryptosystems rely on ramp thresholds, where reconstruction thresholds are strictly greater than secrecy thresholds. For these blockchains, we formally demonstrate that the additional delay is unavoidable. We then introduce a mechanism to minimize this delay in the optimistic case. We implement our optimistic protocol for the proof-of-stake distributed randomness scheme on the Aptos blockchain. Our measurements from the Aptos mainnet show that the optimistic approach reduces latency overhead by 71%.
In this thesis, the aim is to understand the possibilities and limitations of Zero Knowledge Proofs (ZKP) in blockchain technology. The first chapters focus on the presentation of distributed ledger systems and the tools that will be used, with particular reference to Remix, the IDE of choice for programming smart contracts in Solidity, and Sepolia, the Ethereum Testnet where the smart contracts were deployed. Subsequently, zero knowledge proofs are defined, their characteristics are explained, and the difference between interactive and non-interactive proofs is discussed; the latter will be emphasized as they are the most interesting in this context. The thesis then moves on to possible theoretical application contexts, with a particular focus on verifiable computation. Zokrates, a framework used to implement the non-interactive ZKP protocol, will be presented, explaining its language, features, functioning, and differences compared to other programming languages. A focal point for explaining how to implement verifiable computation will be introducing the various representations of an arithmetic circuit, fundamentally of textual and matrix types. Through various tests, the limitations of Zokrates and, to some extent, the entire arithmetic circuits sector for ZKP were highlighted. The thesis concludes with a possible implementation on the subject and an attack that this technology may be able to avoid.
Abstract A zero-knowledge proof protocol is a cryptographic protocol in which a prover, who knows the witness to a statement, can convince a verifier that the statement is true without revealing any information about the witness. Although zero-knowledge proof protocols are typically executed on electronic computers, there is a line of research to design zero-knowledge proof protocols based on physical objects (e.g., a deck of cards). This is called physical zero-knowledge proof. In this paper, we construct a physical zero-knowledge proof protocol for a logical puzzle called Sukoro. Sukoro has many cells on the puzzle board, like Sudoku, where each cell must be empty or filled with a number from one to four, and each number must match the number of adjacent filled cells, and the same numbers must not be adjacent to each other. In addition, it has a rule that all filled cells must be connected, which is called the connectivity condition. Although some existing protocols deal with the connectivity condition, all existing methods are interactive , which requires the proverâs knowledge to determine how the cards are manipulated during the execution of the protocols. In this paper, we give a new method for verifying the connectivity condition in the non-interactive setting, which means that the protocol can be executed without the proverâs knowledge, and construct a physical zero-knowledge proof protocol for Sukoro.
Sizai Hou, Songze Li, Tayyebeh Jahani-Nezhad, Giuseppe Caire
Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data while preserving user privacy. However, the typical paradigm of FL faces challenges of both privacy and robustness: the transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates. Current solutions attempting to address both problems under the one-server FL setting fall short in the following aspects: 1) designed for simple validity checks that are insufficient against advanced attacks (e.g., checking norm of individual update); and 2) partial privacy leakage for more complicated robust aggregation algorithms (e.g., distances between model updates are leaked for multi-Krum). In this work, we formalize a novel security notion of aggregated privacy that characterizes the minimum amount of user information, in the form of some aggregated statistics of users' updates, that is necessary to be revealed to accomplish more advanced robust aggregation. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy. As concrete instantiations of PriRoAgg, we construct two secure and robust protocols based on state-of-the-art robust algorithms, for which we provide full theoretical analyses on security and complexity. Extensive experiments are conducted for these protocols, demonstrating their robustness against various model integrity attacks, and their efficiency advantages over baselines.
Authentication is a crucial security service on the Internet. In real-world applications, multiple independent trust domains often exist, with each recognizing only certain identities within their own systems. During cross-domain access, users cannot directly use their original certificates, which presents a cross-domain authentication problem. Traditional centralized schemes typically employ a trusted third party (TTP) to facilitate the transfer of identity trust across domains. These schemes inevitably inherit the vulnerabilities associated with single points of failure. In contrast, blockchain-based decentralized schemes effectively eliminate the potential threats posed by TTPs. However, the openness and transparency of the blockchain also bring new security issues, such as privacy leakage. In this paper, we propose a zk-SNARK-based anonymous scheme on the blockchain for cross-domain authentication. Specifically, our scheme adopts an authorization-then-proof structure, which strikes a delicate balance between anonymity and revocability. We provide theoretical proofs for the security of our scheme and explain how it achieves proactive revocability. Experimental evaluation results demonstrated that our scheme is both secure and efficient, and the revocation could be accomplished by introducing only 64 bytes of on-chain storage with one hash comparison.
Linh Tran, Sanjay Chari, Md. Saikat Islam Khan, Aaron Zachariah · 6 authors
We present the Differentially Private Blockchain-Based Vertical Federal Learning (DP-BBVFL) algorithm that provides verifiability and privacy guarantees for decentralized applications. DP-BBVFL uses a smart contract to aggregate the feature representations, i.e., the embeddings, from clients transparently. We apply local differential privacy to provide privacy for embeddings stored on a blockchain, hence protecting the original data. We provide the first prototype application of differential privacy with blockchain for vertical federated learning. Our experiments with medical data show that DP-BBVFL achieves high accuracy with a tradeoff in training time due to on-chain aggregation. This innovative fusion of differential privacy and blockchain technology in DP-BBVFL could herald a new era of collaborative and trustworthy machine learning applications across several decentralized application domains.
AI data sharing platforms must reconcile two pressures that often clash: the need to exchange highâvalue datasets for model development and evaluation, and the obligation to guarantee privacy, integrity, and verifiability of computations on that data. This manuscript surveys and synthesizes cryptographic building blocksâdifferential privacy, homomorphic encryption, multiparty computation with secure aggregation, zeroâknowledge proofs, attribute-based encryption and proxy re-encryption, trusted execution environments, and domain standards such as Crypt4GHâinto a pragmatic, layered architecture for AI data sharing. We outline a methodology that integrates policy-aware access control with threshold key management, private training and inference, verifiable analytics, and auditability. A compact statistical analysis (with an illustrative table) demonstrates how such a stack can bound leakage (Δ), preserve utility (accuracy), and manage computational overhead (latency). Results show that a hybrid PETs (privacy-enhancing technologies) approachâcombining local differential privacy and secure aggregation for ingestion, homomorphic encryption or TEEs for computation, and zk-proofs for verifiabilityâachieves strong privacy with modest accuracy loss and acceptable latency for many enterprise scenarios. We conclude with design guidelines and research directions for standards-aligned, future-ready AI data sharing platforms.
D. Dhinakaran, D. Selvaraj, N. Dharini, S. Edwin Raja · 5 authors
The intersection of cloud computing, blockchain technology, and the impending era of quantum computing presents a critical juncture for data security. This research addresses the escalating vulnerabilities by proposing a comprehensive framework that integrates Quantum Key Distribution (QKD), CRYSTALS Kyber, and Zero-Knowledge Proofs (ZKPs) for securing data in cloud-based blockchain systems. The primary objective is to fortify data against quantum threats through the implementation of QKD, a quantum-safe cryptographic protocol. We leverage the lattice-based cryptographic mechanism, CRYSTALS Kyber, known for its resilience against quantum attacks. Additionally, ZKPs are introduced to enhance data privacy and verification processes within the cloud and blockchain environment. A significant focus of this research is the performance evaluation of the proposed framework. Rigorous analyses encompass encryption and decryption processes, quantum key generation rates, and overall system efficiency. Practical implications are scrutinized, considering factors such as file size, response time, and computational overhead. The evaluation sheds light on the framework's viability in real-world cloud environments, emphasizing its efficiency in mitigating quantum threats. The findings contribute a robust quantum-safe and ZKP-integrated security framework tailored for cloud-based blockchain storage. By addressing critical gaps in theoretical advancements, this research offers practical insights for organizations seeking to secure their data against quantum threats. The framework's efficiency and scalability underscore its practical feasibility, serving as a guide for implementing enhanced data security in the evolving landscape of quantum computing and blockchain integration within cloud environments.
Lorenzo Cassano, Jacopo DâAbramo, Siraj Munir, Stefano Ferretti
In this paper, we present a study of a Federated Learning (FL) system, based on the use of decentralized architectures to ensure trust and increase reliability. The system is based on the idea that the FL collaborators upload the (ciphered) model parameters on the Inter-Planetary File System (IPFS) and interact with a dedicated smart contract to track their behavior. Thank to this smart contract, the phases of parameter updates are managed efficiently, thereby strengthening data security. We have carried out an experimental study that exploits two different methods of weight aggregation, i.e., a classic averaging scheme and a federated proximal aggregation. The results confirm the feasibility of the proposal.
To be useful and widely accepted, automated contact tracing schemes (also called exposure notification) need to solve two seemingly contradictory problems at the same time: they need to protect the anonymity of honest users while also preventing malicious users from creating false alarms. In this paper, we provide, for the first time, an exposure notification construction that guarantees the same levels of privacy and integrity as existing schemes but with a fully malicious database (notably similar to Auerbach et al. CT-RSA 2021) without special restrictions on the adversary. We construct a new definition so that we can formally prove our construction secure. Our definition ensures the following integrity guarantees: no malicious user can cause exposure warnings in two locations at the same time and that any uploaded exposure notifications must be recent and not previously uploaded. Our construction is efficient, requiring only a single message to be broadcast at contact time no matter how many recipients are nearby. To notify contacts of potential infection, an infected user uploads data with size linear in the number of notifications, similar to other schemes. Linear upload complexity is not trivial with our assumptions and guarantees (a naive scheme would be quadratic). This linear complexity is achieved with a new primitive: zero knowledge subset proofs over commitments which is used by our "no cloning" proof protocol. We also introduce another new primitive: set commitments on equivalence classes, which makes each step of our construction more efficient. Both of these new primitives are of independent interest.
Farhana Javed, Josep ManguesâBafalluy, Engin Zeydan, Luis Blanco
In the domain of Collaborative Artificial Intelligence, Federated Learning ($\mathbf{F L}$) is a technique that enables multiple entities to collaboratively refine AI models while adhering to stringent data privacy standards, without the need for direct data sharing. This paper explores the integration of blockchain technology with FL to establish reliable trust mechanisms within this collaborative framework. We highlight and review current blockchain-enabled reputation mechanisms that evaluate the reliability and quality of contributions from participants, which are crucial for maintaining trust and operational integrity in distributed settings. Through our review, we address the concept and implementation challenges. Additionally, we discuss recent technological advances and explore the emerging opportunities that blockchain presents to address trust-related challenges in FL, emphasizing significant prospects for future research directions, such as decentralized identities, zero trust, and zero-knowledge proofs to enhance trust in these environments.
Fischlin's transform (CRYPTO 2005) is an alternative to the Fiat-Shamir transform that enables straight-line extraction when proving knowledge. In this work we focus on the problem of using the Fischlin transform to construct UC-secure zero-knowledge from Sigma protocols, since UC security â that guarantees security under general concurrent composition â requires straight-line (non-rewinding) simulators. We provide a slightly simplified transform that is much easier to understand, and present algorithmic and implementation optimizations that significantly improve the running time. It appears that the main obstacles to the use of Fischlin in practice is its computational cost and implementation complexity (with multiple parameters that need to be chosen). We provide clear guidelines and a simple methodology for choosing parameters, and show that with our optimizations the running-time is far lower than expected. For just one example, on a 2023 MacBook, the cost of proving the knowledge of discrete log with Fischlin is only 0.41ms (on a single core). This is 15 times slower than plain Fiat-Shamir on the same machine, which is a significant multiple but objectively not significant in many applications. We also extend the transform so that it can be applied to batch proofs, and show how this can be much more efficient than individually proving each statement. We hope that this paper will both encourage and help practitioners implement the Fischlin transform where relevant.
The sharing of private information is a daunting, multifaceted, and expensive undertaking. Furthermore, identity management is an additional challenge that poses significant technological, operational, and legal obstacles. Present solutions and their accompanying infrastructures rely on centralized models that are susceptible to hacking and can hinder data control by the rightful owner. Consequently, blockchain technology has generated interest in the fields of identity and access control. This technology is viewed as a potential solution due to its ability to offer decentralization, transparency, provenance, security, and privacy benefits. Nevertheless, a completely decentralized and private solution that enables data owners to control their private data has yet to be presented. In this research, we introduce DeAuth, a novel decentralized, authentication and authorization scheme for secure private data transfer. DeAuth combines blockchain, smart-contracts, decentralized identity, and distributed peer-to-peer (P2P) storage to give users more control of their private data, and permissioning power to share without centralized services. For this scheme, identity is proven using decentralized identifiers and verifiable credentials, while authorization to share data is performed using the blockchain. A prototype was developed using the Ethereum Blockchain and the InterPlanetary Files System, a P2P file sharing protocol. We evaluated DeAuth through a use-case study and metrics such as security, performance, and cost. Our findings indicate DeAuth to be viable alternative to using centralized services; however, the underlying technologies are still in its infancies and require more testing before it can supplant traditional services.
Bei Chen, Gaolei Li, Xi Lin, Zheng Wang · 5 authors
Recent advancements in multi-agent systems based on large language models (LLM) have shown potential for problem-solving and planning tasks. However, most existing LLM-based multi-agent approaches show vulnerability against byzantine attacks. First, agents instantiated on diverse LLMs may inherit biases present in the LLMs and thus exhibit deception behavior. Second, as the number of agents grows, collusive behavior among multiple malicious agents poses a potential threat. In this paper, we propose BlockAgents, an innovative framework that integrates blockchain into LLM-based cooperative multi-agent systems to mitigate byzantine behaviors. BlockAgents completes multi-agent collaboration through a unified workflow including role assignment, proposal statement, evaluation, and decision-making. To help the agent who contributes the most to the group thinking process acquire accounting rights, we propose a proof-of-thought (PoT) consensus mechanism combined with stake-based miner designation and multi-round debate-style voting. To effectively distinguish valid and abnormal answers, we design a multi-metric prompt-based evaluation method for each evaluator to score each proposal by carefully and comprehensively considering multiple dimensions. Experiments on three datasets show that BlockAgents reduces the interference of poisoning attacks on accuracy to less than 3% and reduces the success rate of backdoor attacks to less than 5%, demonstrating the resistance ability against Byzantine attacks.