The article presents a comprehensive analysis of the transition from traditional centralized digital identity models to an innovative decentralized paradigm based on block-chain technologies and zero-knowledge proofs (ZKP). It highlights the fundamental problems of existing systems that rely on centralized registries, passwords, and social logins. Such approaches create significant vulnerabilities, including risks of data breaches, mass surveillance, and manipulation, as centralized intermediaries act as sole controllers of personal information, depriving users of control over their data. In response to these challenges, the article discusses the concept of Decentralized Identity (DID). This model enables individuals to own, store, and control their digital credentials independently, without involving intermediaries. The key technological components of this ecosystem include Verifiable Credentials (VC), Digital ID Wallets, and Decentralized Identifiers (DID), which are typically stored on a block-chain to ensure immutability and security. A triadic trust model involving the Issuer, Holder, and Verifier is described, allowing data verification without direct contact with the issuing organization. Special attention is given to the concept of Self-Sovereign Identity (SSI) as a specific philosophy within DID that emphasizes user autonomy, data minimization, and privacy by design. Unlike the broader DID concept, in the SSI model, the user makes the final decision regarding the disclosure of their data. A central technology ensuring privacy in decentralized systems is zero-knowledge proofs (ZKP). ZKP allow the validation of the truthfulness of a statement without revealing the underlying information. The article provides a detailed analysis of the benefits of using ZKP in the context of DID, including selective attribute disclosure (e.g., proving legal age without revealing the date of birth), minimizing the amount of shared data, preventing correlation and user activity tracking, as well as creating reputation systems that preserve anonymity. Practical application scenarios such as private electronic voting and confidential medical data protection are examined. The paper also addresses standardization, which is key to ensuring compatibility and widespread adoption of DID solutions. Leading initiatives such as W3C Verifiable Credentials, the Decentralized Identity Foundation (DIF), and projects like Hyperledger Indy and Aries are mentioned. Examples of advanced implementations already in use are provided: Polygon’s zkKYC for private verification in DeFi, the Sismo protocol for creating anonymous reputation badges in Web3, and Evernym’s SSI platform based on Hyperledger Indy. In conclusion, it is emphasized that the combination of DID and ZKP forms a new paradigm for digital identity management focused on security and user autonomy. Despite challenges related to usability complexity, key loss risk, and legal uncertainty, the technology is actively evolving and moving from conceptual to practical application, which may eventually become the foundation for a global sovereign digital identity.
Dinu-Ştefan Rusu, Emilia-Oana MÎŞŞ, Andrei Vasilățeanu
Ownership of medical data is one of the most important things a person should aim to achieve. An application that allows the patient to be in full control of his data whilst allowing medical personnel to see it such that they can respond accordingly represents a milestone in the development of a smart and integrated emergency system. Such a system should hold the entire patient data, such as prescriptions, vital data and doctor appointments. In this paper we present a system that uses distributed ledger technology to handle medical data such as prescriptions and patient vital data and we showcase how large language models can be used with these technologies to provide a natural interface for the users whilst allowing the user to be in-control of his data. We have also studied how zero knowledge proofs would improve the use case in which a user presents his prescription to a pharmacist.
In the domain of authentication, information leakage which can lead to identity theft represents a significant challenge in the field of cybersecurity. This challenge is particularly relevant in the context of 5 G tactical bubbles, where secure and efficient authentication mechanisms are critical to gain access to sensitive information and communication services. The concept of Zero-Knowledge Proofs, in particular non-interactive proofs, has gained attention in recent years as robust cryptographic methods for privacy-preserving protocols. Zero-knowledge proofs enable users to prove possession of specific knowledge to verifiers without revealing the knowledge itself in a single interaction round. Despite its growing popularity, Zero-Knowledge Proofs have not yet been fully explored within 5 G tactical bubbles. In this paper, we perform a comparative analysis between traditional authentication mechanisms and Zero-Knowledge Proofs-enabled authentications. To this end, we evaluate the feasibility in terms of time and computational complexity and determine whether these advanced authentication protocols can ensure enhanced privacy and security in 5 G tactical bubbles.
Abstract Keeping medical data safe and private has become a big challenge with the fast-growing digital healthcare systems. Old security methods are not strong enough to stop hacking and unauthorized access. This paper introduces an Integrated Privacy Preservation and Blockchain (IPPB) model to make medical data sharing secure. The model uses blockchain to create a decentralized and unchangeable record that ensures data accuracy, precise tracking, and controlled access. It also applies strong encryption methods like homomorphic encryption and zero-knowledge proofs (ZKP) to protect patient information while allowing safe data sharing between healthcare providers. A lightweight approval method is used to make the system faster, which lowers the usual heavy processing of blockchain networks, making it better for real-time medical use. An intelligent contract- based access system ensures that only allowed users can see the data while keeping track of who accessed it. Tests show that the IPPB model works better than current security methods by improving speed, maintaining private data, and defending against cyber threats. The results prove that adding blockchain to privacy systems can make medical data more secure, trustworthy, and easy to share, making it useful for future healthcare systems. Keywords: Privacy Preservation, Blockchain, Medical Data Security, Homomorphic Encryption, Smart Contracts, Secure Data Transmission, Cybersecurity in Healthcare.
The widespread adoption of e-health systems raises critical concerns regarding data privacy and network security. Ensuring secure and reliable data sharing between patients and healthcare professionals remains a significant challenge. To address this, we propose a novel anonymous authentication scheme tailored for e-health environments, integrating zero-knowledge proof (ZKP) with multimodal biometrics. Our key contributions are as follows: (1) applying the Pedersen vector commitment algorithm to construct a biometric-based ZKP scheme, thereby ensuring enhanced security and privacy-preserving authentication; (2) utilizing multimodal cancelable biometrics generate (MCBG) technology, integrating fingerprint, face, and iris modalities to strengthen the security of the verification process; and (3) providing a detailed security analysis that demonstrates our scheme meets essential security requirements, including anonymity, authenticity, unlinkability, forward security, and resistance to replay attacks. Experimental results demonstrate stable proving and verification time of approximately 78 ms and 140 ms, respectively, regardless of the proof range, validating its efficiency and practicality for secure authentication in e-health systems.
This paper presents a formalised architecture for synthetic agents designed to retain immutable memory, verifiable reasoning, and constrained epistemic growth. Traditional AI systems rely on mutable, opaque statistical models prone to epistemic drift and historical revisionism. In contrast, we introduce the concept of the Merkle Automaton, a cryptographically anchored, deterministic computational framework that integrates formal automata theory with blockchain-based commitments. Each agent transition, memory fragment, and reasoning step is committed within a Merkle structure rooted on-chain, rendering it non-repudiable and auditably permanent. To ensure selective access and confidentiality, we derive symmetric encryption keys from ECDH exchanges contextualised by hierarchical privilege lattices. This enforces cryptographic access control over append-only DAG-structured knowledge graphs. Reasoning is constrained by formal logic systems and verified through deterministic traversal of policy-encoded structures. Updates are non-destructive and historied, preserving epistemic lineage without catastrophic forgetting. Zero-knowledge proofs facilitate verifiable, privacy-preserving inclusion attestations. Collectively, this architecture reframes memory not as a cache but as a ledger - one whose contents are enforced by protocol, bound by cryptography, and constrained by formal logic. The result is not an intelligent agent that mimics thought, but an epistemic entity whose outputs are provably derived, temporally anchored, and impervious to post hoc revision. This design lays foundational groundwork for legal, economic, and high-assurance computational systems that require provable memory, unforgeable provenance, and structural truth.
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.
The insurance sector has been moving towards a more digital footing on the backdrop of growing demand for transparency, efficiency and privacy of data. The common way it is built exposes it to subtle policy checks and human intervention for claim reviews and exposes consumer data. Although blockchain technology has been explored as a possible solution, the public log nature of blockchain introduces grave concerns in privacy preservation, especially, in industries with strict regulatory requirements, including healthcare and life insurance. We propose a privacy-preserving insurance system in our paper based on ZKPs and SMPC to handle these issues. Zero-Knowledge Proofs (ZKPs) enable policyholders to prove to third parties’ compliance to their policy conditions without revealing underlying information, while leveraging Secure Multi-Party Computation (SMPC) provides insurance companies, business partners, and third-party auditors to jointly compute premium prices, validate claims, and compute refunds on privatized data inputs. End to End the Privacy of Data is secured, even at point of payment, Claim or Refund settlement. We present experimental results demonstrating real-time (300-950ms) latency, perfect replication on all nodes, and no leakage across transaction types. This work addresses inefficiencies that have plagued the insurance industry for years by presenting an extensible, cryptographically secure technology that can be used in the next era of digital insurance. It proposes a verifiable secret sharing based transaction model to bring in a new trustful and privacy-preserving insurance transaction paradigm.
This paper presents a comprehensive examination of advanced cryptographic primitives and their instantiations, focusing on signature schemes, zero-knowledge proofs, and threshold encryption schemes. The foundational properties required for a secure and complete digital signature scheme are first outlined, with an emphasis on existential unforgeability. Zero-knowledge proofs are then examined in detail, including definitions of completeness, zero-knowledge, soundness, and simulation extractability, along with a discussion of universal versus non-universal proof systems. The core contribution lies in the design and analysis of a threshold encryption scheme based on Shamir’s secret sharing and a CLT encryption framework. The construction of a (t, P)-threshold encryption scheme is defined, introducing the concept of partial decryption simulability. The framework ensures that any (t + 1)-sized subset of parties can decrypt the ciphertext while maintaining security against adversarial attempts. Additionally, the implementation of universal zero-knowledge proof systems is discussed, highlighting the trade-offs between universal and specific SRS-based proofs. The instantiation of pseudorandom functions and their weak robustness properties is also examined, ensuring secure key management and resistance to adversarial key collisions. Through detailed analysis and construction, this work provides a solid foundation for building secure cryptographic systems with efficient threshold encryption and zero-knowledge proofs, contributing to the advancement of cryptographic protocols and their applications in secure communications and data protection.
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.
Performing effective supervision in blockchain networks while preserving public anonymity has long been a challenging issue. Existing solutions often depend on third-party institutions or dual-chain architectures to monitor and recover user identities, but these approaches pose significant risks, such as identity disclosure and increased inter-chain communication costs. To address these concerns, this paper proposes a novel supervised scheme based on Threshold Secret-Sharing (TSS) and Zero-Knowledge Proof (ZKP) on anonymous accounts on the blockchain called TSS-ZKP, while integrating regulatory authority (RA) and multiple traceability centers (TC). Compared to traditional single RA, TSS-ZKP decentralizes the regulatory authority and storage across multiple TCs, significantly reducing the risk of user identity leakage by a single RA. By TSS-ZKP, user identities are recovered through the collaborative generation of sub-secrets by the RA and TCs, without storing actual identity information. By leveraging elliptic curves and using hash functions as secret labels, TSS-ZKP achieves lightweight operations, allowing TCs to efficiently locate sub-secrets without extensive traversal. The comprehensive analysis of security and privacy demonstrated that TSS-ZKP effectively safeguards user privacy while enabling feasible supervision. In addition, comparison experimental results show that the time consumption of the TSS-ZKP is about 30% of that of the comparison scheme. Meanwhile, the simulation results highlight the practicality of the scheme, showing that TSS-ZKP significantly reduces the delays in the identity recovery process while maintaining high usability. In general, TSS-ZKP provides a safer and more feasible solution for enabling the supervision of blockchain accounts.
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.
Road-Side Units (RSUs) are deployed along the road to facilitate Vehicle-to-Infrastructure (V2I) communication, a critical component of Vehicle-to-Everything (V2X) services. However, the presence of rogue RSUs, which are unauthorized access points, poses significant threats to V2X communications and safety applications. These rogue RSUs, installed by adversaries, can mimic legitimate RSUs and establish connections with vehicles, enabling various attacks such as data interception, spoofing, and denial of service. Therefore, Software-defined Networking (SDN) has been leveraged to employ various traffic engineering, network management, and secure verification of RSUs and vehicle functionalities. The SDN controller (SDNC), which manages RSUs, can periodically verify their identity. This mechanism ensures the association of vehicles with legitimate RSUs and the detection of rogue RSUs. To periodically verify the RSUs' identity, a novel Fiat-Shamir Transformation-enabled Non-Interactive Zero-knowledge Proof ($\text{Z K P}$) -based identity verification mechanism has been proposed. The RSUs are initially registered with an SDNC in this protocol. Subsequently, SDNC verifies their identity periodically using a unique ZKP-based challenge-response mechanism. As per the performance and security analysis, the proposed protocol surpasses state-of-the-art authentication protocols and achieves notable improvements.
Ningyuan Chen, Chiew Foong Kwong, David Chieng, Pushpendu Kar · 6 authors
The advent of Cellular Vehicle-to-Everything (CV2X) technology has revolutionised intelligent transportation systems (ITS), but poses challenges for secure and efficient data sharing due to its dynamic nature. Traditional centralised systems are inadequate, prompting the need for decentralised solutions like blockchain. However, applying blockchain technologies in C-V2X always faces scalability issues. This paper proposes a scalable C-V2X blockchain network with a hierarchical consensus by integrating a dynamic load-balancing sharding mechanism and zero-knowledge proofs (ZKPs). Our scheme ensures scalability in the C-V2X environment through sharding while utilising ZKPs to enhance cross-shard validation efficiency, reducing its complexity to$O(1)$. Additionally, our approach reduces bandwidth consumption by 90.8% compared to Merkle tree-based solutions and its consensus time is lower than 360 ms.
The recent machine learning requires huge machine resources and is often hard for users with limited resources. Although there are DPT, these are unsuitable for a situation where a trained model should be unrevealed from other users. In this paper, we first propose a new learning method, aggregated zero-knowledge deep learning (AZKDL), whereby even a user with a limited resource contributes to the learning process without revealing its model. Our main idea is to utilize aggregated zero-knowledge proofs where individual zero-knowledge proofs are aggregated into a single proof. Loosely speaking, users generate proofs for their training of parts of models and then aggregate both the models and the proofs to verify the entire models without revealing them. We also prove that AZKDL can detect malicious training. When we conduct experiments to evaluate AZKDL, we identify that even a client with the largest model parameters can finish the computation within a second. Furthermore, we propose the distributed proof-of-deep-learning (DPoDL) that rewards users who contribute to the learning process by applying AZKDL to a mining process of blockchains. DPoDL can detect malicious users by AZKDL.