Thomas Haines, Rafieh Mosaheb, Johannes Müller, Reetika
Zero-Knowledge Proofs (ZKPs) are cryptographic building blocks of many privacy-preserving security protocols. An important research focus in this area is the development of post-quantum ZKPs. These are ZKPs whose security is reduced to computational hardness assumptions that are assumed to be intractable even by scalable quantum computers. In this paper, we study the post-quantum ZKPs of Jain, Krenn, Pietrzak, and Tentes (Asiacrypt 2012). These are the only ZKPs for proving arbitrary binary statements whose security reduces to the Learning Parity with Noise (LPN) problem-a very conservative post-quantum hardness assumption. We make the following contributions to further develop the potential and understanding of these ZKPs. First, we optimize the efficiency of the verifier by several orders of magnitude, making this part as computationally light as that of the prover. Second, we show that the only open source implementation of these ZKPs does not implement them correctly, allowing a malicious prover to convince the verifier of false statements. Third, we formally verify for the first time the security of these (optimized) ZKPs in EasyCrypt. Fourth, we show how these ZKPs can be used to construct the first code-based ZKP of shuffle and verifiable e- voting protocol.
The security proof of a protocol, though formally rigorous within a given model, is entirely contingent on the model's assumptions. If the adversary's capabilities are underspecified, the cryptographic primitives are idealized, or the security properties are incompletely formalized, the proof may not hold in practice.The first contribution advances prior work on refining symbolic models for crypto- graphic primitives to better capture their behaviors. Specifically, we propose more precise equational theories for the ElGamal cryptosystem, DSA signatures, and Zero-Knowledge Proofs. Standard symbolic modeling of these primitives disregards their algebraic prop- erties, which may lead to missed attacks in larger protocols. Additionally, we introduce a formal model of exponentiation and re-encryption Mix-Networks. By combining these models with our equational theories, we can automatically find attacks based on the incorrect use of the Mix-Networks missed by previous symbolic models.The second contribution involves analyzing the WireGuard protocol. We examine the protocol's claimed security properties under an adversary capable of compromising any possible key combinations. To systematize this analysis, we introduce the concepts of minimal defensive model and minimal offensive adversary model. The defensive models ensures that violating a security property requires possessing specific atomic capabilities. Minimal offensive models define the smallest sets of adversarial capabilities that break security. Theses derivations helped to identify an implementation optimization that introduces new attack vectors.The third contribution presents a hybrid protocol combining WireGuard and Post- Quantum WireGuard, aligning with recommendations for a secure transition to post- quantum cryptography. Although a symbolic analysis of PQ-WireGuard existed, we uncover discrepancies between the model and the protocol's specifications, including pre- viously missed Unknown Key-Share attacks. We propose fixes and ensure the hybrid protocol's security relies on both the corrected post-quantum and classical WireGuard protocols. We formally defined a hybrid protocol's security as when there exists both minimal defensive models dependent on post-quantum keys and defensive models depen- dent on classical keys. Our work underscores the importance of iterative analysis during design, as achieving hybrid security required repeated refinement between modeling and verification.
Bello Musa Yakubu, Abdullah Alabdulatif, Pattarasinee Bhattarakosol
The rice supply chain is a complex system that demands effective management to ensure reliability and efficiency, given the involvement of multiple stakeholders. Blockchain technology, with its decentralized and tamper-resistant nature, offers a promising solution for improving transparency, traceability, and credibility in agricultural supply chains. However, existing blockchain systems face several technological challenges, including security vulnerabilities, privacy concerns, and performance limitations. To address these issues, this article presents RiceChain-Plus, an enhanced architecture that incorporates a private Ethereum blockchain, proof of authority (PoA) consensus mechanism, mutual authentication, zero-knowledge proofs (ZKPs), a hybrid role-based access control (RBAC) and attribute-based access control (ABAC) system, and one-way hash functions. This approach enhances the rice supply chain's security, privacy, and efficiency by safeguarding sensitive data and ensuring confidentiality. Performance assessments show that RiceChain-Plus surpasses existing benchmark models, achieving the lowest average execution costs (44,634 gas), reduced energy consumption (9.38828E-05 J), higher throughput (0.071201 transactions/s), faster execution (44.5 ms), and quicker transaction times (14.045 s), while also improving scalability. A comprehensive security analysis further confirms the framework's resilience against various cyberattacks. These results highlight RiceChain-Plus as a secure, efficient, and effective solution for optimizing rice supply chain operations.
Dumitrel Loghin, Shuang Liang, S. Liu, Xiong Liu · 6 authors
Zero-knowledge proofs (ZKP) are becoming a gold standard in scaling blockchains and bringing Web3 to life. At the same time, ZKP for transactions running on the Ethereum Virtual Machine require powerful servers with hundreds of CPU cores. The current zkProver implementation from Polygon is optimized for x86-64 CPUs by vectorizing key operations, such as Merkle tree building with Poseidon hashes over the Goldilocks field, with Advanced Vector Extensions (AVX and AVX512). With these optimizations, a ZKP for a batch of transactions is generated in less than two minutes. With the advent of cloud servers with ARM which are at least 10% cheaper than x86-64 servers and the implementation of ARM Scalable Vector Extension (SVE), we wonder if ARM servers can take over their x86-64 counterparts. Unfortunately, our analysis shows that current ARM CPUs are not a match for their x86-64 competitors. Graviton4 from Amazon Web Services (AWS) and Axion from Google Cloud Platform (GCP) are 1.6X and 1.4X slower compared to the latest AMD EPYC and Intel Xeon servers from AWS with AVX and AVX512, respectively, when building a Merkle tree with over four million leaves. This low performance is due to (1) smaller vector size in these ARM CPUs (128 bits versus 512 bits in AVX512) and (2) lower clock frequency. On the other hand, ARM SVE/SVE2 Instruction Set Architecture (ISA) is at least as powerful as AVX/AVX512 but more flexible. Moreover, we estimate that increasing the vector size to 512 bits will enable higher performance in ARM CPUs compared to their x86-64 counterparts while maintaining their price advantage.
Fausto Neri da Silva Vanin, Rodrigo da Rosa Righi, Cristiano André da Costa
Blockchain technology in healthcare is gaining attention for addressing data privacy, interoperability, and health record integrity issues. Standards like HL7 FHIR and OpenEHR ensure data consistency, but privacy concerns persist under regulations like HIPAA, GDPR, and LGPD. Existing methods often store only data hashes, raising validation risks. The MEPCA model introduces a blockchain-based framework for secure health record management, focusing on on-chain EHR data processing. Key elements include Data Steward, Shared Data Vault, and Zero-Knowledge Proofs of HL7 FHIR fields. Experiments with Fully Homomorphic Encryption show enhanced security and reliability for health records, offering a robust alternative to traditional off-chain approaches.
We present a secure and efficient federated learning protocol for autonomous vehicles that resists data leaks, redundancy, and adversarial attacks. Our system combines fast zero-knowledge proofs and compressed Bloom filters to verify updates without exposing private data. Compared to traditional approaches, our method reduces proof sizes by 90 % (under 10 KB), memory by up to 75 %, and maintains accuracy with less than 4% degradation under 30% attack rates. The entire update cycle completes in under 600 ms, making it practical for real-time use in vehicles. This work advances trustworthy AI deployment in dynamic, resource-limited networks.
Stablecoins face an unresolved trilemma of balancing decentralization, stability, and regulatory compliance. We present a hybrid stabilization protocol that combines crypto-collateralized reserves, algorithmic futures contracts, and cross-chain liquidity pools to achieve robust price adherence while preserving user privacy. At its core, the protocol introduces stabilization futures contracts (SFCs), non-collateralized derivatives that programmatically incentivize third-party arbitrageurs to counteract price deviations via adaptor signature atomic swaps. Autonomous AI agents optimize delta hedging across decentralized exchanges (DEXs), while zkSNARKs prove compliance with anti-money laundering (AML) regulations without exposing identities or transaction details. Our cryptographic design reduces cross-chain liquidity concentration (Herfindahl-Hirschman Index: 2,400 vs. 4,900 in single-chain systems) and ensures atomicity under standard cryptographic assumptions. The protocol's layered architecture encompassing incentive-compatible SFCs, AI-driven market making, and zero-knowledge regulatory proofs. It provides a blueprint for next-generation decentralized financial infrastructure.
Efficient and secure sharing of scientific data remains a key challenge in the Open Science framework, especially in terms of data authenticity, provenance and privacy. Traditional digital repositories improve access but often lack decentralized mechanisms that guarantee integrity and traceability. Blockchain technology provides a potential solution through tamper-proof records and distributed consensus, while Zero Knowledge Proofs (ZKP) can enhance privacy protection. This study explores how blockchain and ZKP can be integrated for decentralized scientific data management. A systematic literature review reveals limited application of these combined technologies in Open Science, highlighting a research gap and the need for solutions that support transparent, secure and privacy-preserving data sharing in accordance with FAIR principles.
Ahmed Sayed M. Metwally, Yazeed Alhumaidan, Saad Alzahrani, Mohamed H. Abdelati
Implementing artificial intelligence (AI) and blockchain technology in management systems transforms traditional libraries into advanced information centers that are data-driven and effectively managed. While these technologies enhance efficiency and operational capabilities, they also present two critical challenges: data privacy and ethical concerns. This study examines the role of AI and blockchain in library management, focusing on issues related to data privacy and ethical challenges that arise from their use. It also offers best practices to ensure safe implementation. The research adopts a comprehensive mixed-methods approach, involving qualitative interviews and quantitative surveys to identify these challenges within the system architecture, assess the effectiveness of current designs, and propose a complete framework using privacy-preserving technologies. This framework incorporates innovative cryptographic techniques, including homomorphic encryption, differential privacy, and zero-knowledge proofs, providing a novel model for the ethical use of AI in libraries. The findings indicate that robust data protection, transparency, and accountability are essential to building trust in AI-powered library services.
In this work, we present a concretely efficient lattice-based zero-knowledge proof system that satisfies straight-line extractability. At the heart of our construction is a novel, non-generic application of the Katsumata transform (CRYPTO 2021) to the widely adopted zero-knowledge framework of Lyubashevsky, Nguyen and Plançon (CRYPTO 2022). Our approach significantly optimizes the proof size, achieving a 2X reduction compared to prior generic techniques. This makes our scheme particularly well-suited for anonymous credential systems, secure multi-party computation, and proving quantum security, where straight-line extractability is essential.
A zero-knowledge proof (ZKP) is an interactive proof between a prover and a verifier, with the prover's goal to convince the verifier that a solution of a computation problem exists without revealing any information about the solution itself. A card-based physical ZKP is an implementation of a ZKP using physical cards. Several metrics are used to measure the efficiency of card-based ZKPs, such as the numbers of required cards and shuffles. In this paper, we propose the first card-based ZKP for a puzzle Sudoku that uses only one shuffle. To achieve this, we use a technique of applying private operations interactively to prepare inputs for the protocol.
The existing methods do not effectively meet the security and performance demands for Internet of Vehicles (IoV) applications. They also do not provide low-latency, secure edge-computing solutions for end-users in vehicular environments. The study presented in this paper proposes a blockchain-based edge computing framework that utilises Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) consensus for simultaneously optimising latency, energy consumption, and security. For efficient microservice orchestration and task off-loading, the containerised architecture utilises Kubernetes with Hyperledger Fabric. The experiments conducted in urban, suburban, and highway scenarios confirmed that the proposed framework outperformed baseline algorithms with end-to-end latency reduction of 30–45% while also lowering energy consumption by up to 55% under moderate-to-heavy loads. With less than 1.2 seconds per block on the blockchain consensus, the system also maintained task completion rates exceeding 95% during peak conditions. The framework demonstrates consistent performance across various vehicular densities and consumes zero-knowledge proofs with attribute-based encryption for data against cybersecurity threats. These results confirm that the integration of DDQN and blockchain technology effectively tackles primary obstacles IoV faces by providing secure edge computing for next generation vehicular networks.
Efficiently verifying mathematical proofs and computations has been a heavily researched topic within Computer Science. Particularly, even repetitive steps within a proof become much more complex and inefficient to validate as proof sizes grow. To solve this problem, we suggest viewing it through the lens of Incrementally Verifiable Computation (IVC). However, many IVC methods, including the state-of-the-art Nova recursive SNARKs, require proofs to be linear and for each proof step to be identical. This paper proposes Lova, a novel framework to verify mathematical proofs end-to-end that solves these problems. Particularly, our approach achieves a few novelties alongside the first-of-its-kind implementation of Nova: (i) an innovative proof splicing mechanism to generate independent proof sequences, (ii) a system of linear algorithms to verify a variety of mathematical logic rules, and (iii) a novel multiplexing circuit allowing non-homogeneous proof sequences to be verified together in a single Nova proof. The resulting Lova pipeline has linear prover time, constant verifying capability, dynamic/easy modification, and optional zero-knowledge privacy to efficiently validate mathematical proofs. We offer potential use cases for Lova to secure entire Cyber-Physical Systems (CPS) pipelines, as well as localized CPS systems in automotive and healthcare devices. Code is available at https://github.com/noelkelias/lova.
Ensuring privacy and security in healthcare data sharing is critical due to the sensitive nature of patient information and the growing threat of cyber attacks. This paper explores the development of privacy-preserving data-sharing protocols for healthcare systems by integrating cryptographic techniques and blockchain technology. The study aims to establish a secure framework that facilitates seamless data exchange among healthcare stakeholders while maintaining data integrity, confidentiality, and access control. Key cryptographic mechanisms, including homomorphic encryption, zero-knowledge proofs, and attribute-based encryption, are employed to ensure that only authorized entities can access patient records without exposing sensitive details. Blockchain technology is leveraged to create a decentralized and tamper-resistant ledger, ensuring transparency and auditability in data-sharing transactions. Smart contracts are utilized to enforce predefined access policies automatically, enhancing security and compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). The findings indicate that the proposed framework significantly mitigates risks associated with unauthorized access, data breaches, and single points of failure. Comparative analysis with traditional centralized systems demonstrates improved efficiency, scalability, and security in healthcare data management. The integration of blockchain and cryptographic techniques ensures robust privacy-preserving mechanisms without compromising accessibility or interoperability. This research provides a novel approach to secure data sharing in healthcare, fostering trust among stakeholders while ensuring compliance with privacy regulations. Future work will focus on optimizing computational efficiency and addressing scalability challenges to facilitate widespread adoption in real-world healthcare ecosystems.
Web-based credit card payments require complete disclosure of all payment card details for transaction authorization. The card’s CVV (Card Verification Value) is the secret code that authorizes card not presented transactions. Currently, all payment card details must be shared among various intermediaries involved in processing the transaction. To mitigate the risks associated with fraudulent transactions, industries have adopted security standards such as the PCI DSS. Credit card data confidentiality rests on all involved stakeholders adhering to best security practices, including data communication encryption, and do not misuse the payment information. However, this security posture does not prevent potential credit card data leaks. We propose an alternative method for conducting remote card payments that does not require disclosing the authorization code while ensuring high interoperability with existing payment networks. Our approach demonstrates how designated verifier Zero-Knowledge Proofs (ZKP) enable minimal disclosure of card details, particularly protecting the confidentiality of authorization codes.
Daria Schumm, Cedric von Rauscher, Katharina Olga Emilia Müller, Burkhard Stiller
Transparency and immutability of blockchains can expose metadata and raise concerns about its classification as personal data under privacy regulations. This paper investigates privacy risks associated with metadata in blockchain-based identity systems. Additionally, two privacy-preserving mechanism designs, namely Zero-Knowledge Proof (ZKP) and Homomorphic Encryption (HE), to protect metadata are proposed. As a result, this work introduces the first use case of HE privacy-preserving mechanism in the context of Decentralized Identity (DI) and Self-Sovereign Identity (SSI) systems.
Nai‐Wei Lo, Chi-Ying Chuang, Jheng-Jia Huang, Yuxuan Luo
With the rise of the Internet of Vehicles (IoV), secure and efficient authentication is essential to prevent cyber threats. This paper proposes a session key establishment protocol using Zero-Knowledge Proofs (zk-SNARKs) and Elliptic Curve Cryptography (ECC), including the Elliptic Curve Diffie–Hellman (ECDH) key exchange, to ensure privacy and efficiency. While zk-SNARK computations introduce additional verification overhead, our optimizations, such as precomputed proof parameters and lightweight session re-authentication, mitigate delays. Performance evaluation shows a 20% reduction in computation overhead and a 75% faster re-authentication time compared to existing methods, making it a secure and practical solution for real-world IoV applications.
As the U.S. healthcare system shifts toward value-based care, there is a growing need for patient-centered technologies that ensure data ownership, interoperability, and trust.This paper proposes a novel framework integrating neuro-symbolic artificial intelligence (AI) and zero-knowledge (ZK) blockchain to enable a secure, scalable, and ethically grounded digital-twin marketplace for patient data.The goal is to empower individuals to own and control their health digital twins-comprehensive, dynamic, AI-driven models representing real-time physiological, behavioral, and clinical states-while facilitating precision care and research collaborations.At a macro level, the framework leverages neuro-symbolic AI to enhance digital twin reasoning, enabling explainable predictions and treatment simulations across diverse datasets.This is paired with ZK-proof blockchain infrastructure to ensure privacy-preserving authentication, decentralized governance, and monetization of patient data without revealing sensitive health information.The integration addresses key challenges in trust, transparency, and consent in patient-provider and patient-researcher relationships.Zooming into operational layers, the paper outlines a decentralized application (dApp) architecture that supports smart contracts for patient-informed data sharing, automated payer-provider interactions, and regulatory compliance tracking.It also highlights how incentives within the marketplace can align with care quality metrics, promote social determinants of health inclusion, and advance equitable data access in underrepresented populations.Case scenarios in chronic disease management and clinical trials illustrate the feasibility of this patient-owned digital-twin ecosystem.Ethical considerations, including algorithmic fairness, data sovereignty, and digital consent protocols, are also critically examined.By combining symbolic logic, neural learning, and cryptographic assurance, this framework sets the foundation for a secure and equitable next-generation health economy.
Krzysztof Gogol, Szczepan Gurgul, Faizan Nehal Siddiqui, David Branes · 5 authors
Ethereum's scalability limitations pose significant challenges for the adoption of decentralized applications (dApps). Zero-Knowledge Rollups (ZK Rollups) present a promising solution, bundling transactions off-chain and submitting validity proofs on-chain to enhance throughput and efficiency. In this work, we examine the technical underpinnings of ZK Rollups and stress test their performance in real-world applications in decentralized finance (DeFi). We set up a proof-of-concept (PoC) consisting of ZK rollup and decentralized exchange, and implement load balancer generating token swaps. Our results show that the rollup can process up to 71 swap transactions per second, compared to 12 general transaction by Ethereum. We further analyze transaction finality trade-offs with related security concerns, and discuss the future directions for integrating ZK Rollups into Ethereum's broader ecosystem.
As blockchain technology drives the global expansion of the digital currency market, the widespread adoption of high-frequency trading and cross-market arbitrage strategies poses dual challenges to traditional regulatory measures in terms of timeliness and accuracy. This study constructs a hybrid neural network model that integrates supervised and unsupervised learning to explore multi-dimensional feature fusion paths between on-chain data from blockchain and secondary market price data. Based on dynamic game theory, an intelligent regulatory sandbox system is designed, incorporating on-chain address reputation scoring mechanisms and liquidity smart contract circuit breakers to achieve real-time warnings and responses to market manipulation behaviors. Furthermore,a distributed regulatory framework built on zero-knowledge proof technology is proposed, providing a feasible solution for establishing a penetrating regulatory system while ensuring transaction privacy.
The rapid growth of artificial intelligence (AI) in decentralized systems such as healthcare, financial networks, and autonomous transportation has underscored the critical need for interpretability, fairness, and verifiable trust in decision-making. Traditional federated learning frameworks, while addressing data privacy and scalability, often suffer from bias propagation, opaque model behaviors, and limited mechanisms for ensuring accountability. This article introduces Chain-of-Trust AI, a novel paradigm that integrates zero-knowledge proofs (ZKPs), federated reinforcement learning (FRL), and generative learning models to create an interpretable, bias-free, and verifiable decision-making framework for complex distributed environments. The proposed framework leverages FRL to enable adaptive coordination across heterogeneous agents while maintaining local data sovereignty. Generative learning models, such as variational autoencoders, provide transparent causal representations that support bias detection and enhance interpretability of reinforcement-driven policies. ZKPs are embedded as cryptographic guarantees to verify model updates and decision outcomes without exposing sensitive information, thus ensuring compliance, trust, and transparency across decentralized networks. Methodologically, the framework is evaluated through MATLAB-based multi-agent simulations, benchmarking performance in terms of interpretability, fairness indices, convergence stability, and verification overhead. Theoretical analyses confirm convergence under heterogeneous reward structures, cryptographic soundness of proofs, and bias reduction capabilities through generative regularization. Case studies in decentralized healthcare diagnostics, financial fraud detection, and autonomous vehicular coordination highlight the practical scalability and robustness of Chain-of-Trust AI. By uniting reinforcement learning, generative interpretability, and zero-knowledge verification, this work pioneers a secure, auditable, and ethically aligned AI architecture for decentralized complex systems, advancing both technical rigor and governance in distributed intelligence.
Distributed trust systems have been transformed by blockchain technology; however, scalability and privacy preservation remain major obstacles. Blockchain-based ridesharing platforms, which provide decentralization, privacy, and enhanced user control inside the system, have been offered as a solution to these problems. Blockchain-based ridesharing services have scaling problems in spite of these benefits. These systems' performance declines with an increase in users, which restricts their usefulness in high-volume marketplaces. To overcome these constraints, this study investigates how blockchain topologies can use zero-knowledge proof (ZKP) approaches. We provide a thorough examination of the current ZKP implementations in blockchain systems, such as zk-Rollups and Bulletproofs, assessing their theoretical underpinnings, real-world uses, and performance indicators. Our study shows that although ZKP integration can increase throughput through rollup technologies and greatly improve privacy guarantees, computational overhead and implementation difficulties continue to be obstacles to broad adoption. Provide a framework for ZKP integration that is tailored to balance privacy, scalability, and usability. This could move blockchain technology closer to more useful real-world applications.