Blockchain-based voting systems provide transparency and auditability but introduce significant privacy risks due to publicly observable metadata. Existing approaches rely on mixnets or heavy cryptographic primitives to achieve anonymity, resulting in high computational overhead and limited scalability. In this paper, we propose a novel privacy-preserving voting protocol that eliminates the need for full ciphertext mixnets by introducing a selective metadata mixing mechanism. Our protocol combines zero-knowledge proofs for vote validity, homomorphic encryption for confidential aggregation, and randomized metadata transformations to achieve unlinkability. We formalize security properties including ballot secrecy, unlinkability, and end-to-end verifiability, and prove security under standard cryptographic assumptions. We further provide a gas-aware smart contract model and evaluate scalability for elections with one million voters under Layer-2 rollup deployment. Our results show that the proposed protocol reduces anonymization complexity from O(n log n) to O(n) while maintaining strong privacy guarantees.
Financial exclusion remains acute in Sub-Saharan Africa, where more than 350 million adults lack access to formal financial services. A defining barrier is the absence of verifable identity: in countries such as Mozambique, Tanzania, and Niger, over half of adults without mobile money accounts cite missing documentation as the primary obstacle. Existing remedies either centralise sensitive personal data, creating systemic privacy and security risks, or demand document-issuing infrastructure that does not yet exist in many communities. This paper proposes ZKP-IDFS (Zero-Knowledge Proof Identity for Financial Services), a decentralised, privacy preserving digital identity framework that lets individuals prove identity-related predicates to financial institutions without disclosing the underlying personal attributes. ZKP-IDFS combines Groth16 zk-SNARKs for succinct on-chain proof verification, Pedersen commitments for attribute hiding, a W3C-compliant Verifi-able Credential layer, and a lightweight USSD/SMS proof-relay channel designed for feature-phone users in low-connectivity environments. We formalise the cryptographic model, specify the system architecture, and present a simulated performance evaluation across four representative network conditions. Results from a controlled simulation study show that end-to-end proof generation and relay complete in under 4.2 seconds on entry-level Android handsets at 3G speeds, with on-chain verication costs below 0.003 USD on an EVM-compatible layer-2 chain; these results require validation in eld deployments. We further demonstrate compliance with FATF risk-based KYC guidance and with emerging African data-protection legislation.
Digital identity is critical, yet centralized providers create single points of failure—breaches have exposed billions of records—and quantum computing threatens the classical public-key cryptography (RSA/ECC) on which these systems rely. We present a system-level integration of blockchain, zero-knowledge proofs (ZKPs), and post-quantum cryptography (PQC) for privacy-preserving digital identity. A blockchain-based decentralized identifier (DID) system removes central databases; all signing and key-encapsulation operations use lattice-based PQC (CRYSTALS-Dilithium and Kyber); and selective disclosure is provided by Groth16 zk-SNARKs, with revocation via on-chain Merkle non-membership accumulators. We specify the full credential lifecycle—issuance, two-phase authentication, and revocation—with an explicit trust boundary separating the in-circuit Groth16 relation from the off-circuit issuer-signature check. We report a measured evaluation on a reference prototype: under liboqs 0.15.0, Dilithium-II signs/verifies in 0.19/0.06 ms and Kyber-512 encapsulates/decapsulates in 0.018/0.022 ms; a single-authentication Groth16 proof over the 21,715-constraint BN254 credential circuit takes <inline-formula> <tex-math notation="LaTeX">$\approx 981$ </tex-math></inline-formula> ms (snarkJS) and <inline-formula> <tex-math notation="LaTeX">$\approx 177$ </tex-math></inline-formula> ms (native rapidsnark) on byte-identical inputs, with <inline-formula> <tex-math notation="LaTeX">$\approx 40$ </tex-math></inline-formula> ms verification, a 723-byte proof, and <inline-formula> <tex-math notation="LaTeX">$\approx 243$ </tex-math></inline-formula>,000 gas for on-chain verification on a local EVM. A lifecycle harness with a passing revoked-credential negative test validates correctness. The signing and key-encapsulation layers are quantum-safe under current lattice assumptions; the Groth16 proof layer is classically secure only, and its post-quantum migration is identified as future work. End-to-end credential unforgeability is conditioned on an honest holder wallet performing the off-circuit signature check (Assumption 5). Every quantitative claim is labelled measured [M], simulated [S], assumption [A], or future work [F].
Andrea Esposito, Francesco P. Rossi, Marco Bernardo, Francesco Fabris · 5 authors
Algorand is a scalable and secure permissionless blockchain that achieves proof-of-stake-based consensus via binary Byzantine agreement and cryptographic self-sortition. In this paper we present a process algebraic model of the Algorand consensus protocol, which captures the behavior of participants in terms of the alternation of steps toward a committee-based agreement. We use the model to study the robustness of the protocol with respect to malicious participants, which may try to boy- cott the commitment of the proposed block, as well as the probabilities of committing the proposed block or an empty one after a boycott attempt. Our process algebraic model is translated into LNT, the language of the CADP toolset, to investigate robustness via a novel application of equivalence-checking-based noninterference analysis, which we have implemented in CADP through its script verification language SVL.
The proliferation of distributed multi-agent systems in industrial and healthcare domains highlights fundamental limitations of centralized authentication architectures. These systems, comprising autonomous agents operating across organizational boundaries, require authentication mechanisms that eliminate single points of failure, preserve data sovereignty, protect privacy during data aggregation, and enable trust establishment without central authorities. Central identity providers, however, introduce systemic risks by concentrating trust and control, enabling privacy-invasive observation of authentication events and, in the event of compromise, facilitating large-scale credential breaches, challenges that are particularly acute in scalability- and privacy-sensitive deployments. This thesis presents the design, implementation, and evaluation of the Distributed Authentication and Privacy System (DAPS), a decentralized authentication framework for multi-agent systems based on W3C Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). The research adopts a Design Science Research (DSR) methodology and contributes a reference architecture together with a corresponding implementation on Hyperledger Fabric, a permissioned enterprise blockchain platform that does not natively support Self-Sovereign Identity (SSI). DAPS implements a three-component architecture comprising autonomous agents, fusion centers as data aggregators, and credential issuers. Agents generate cryptographic keys and DIDs, obtain issuer-signed Verifiable Credentials, and authenticate with fusion centers using a decentralized authentication protocol that does not require contacting credential issuers at the time of verification. Credential integrity and revocation status are validated through blockchain-anchored proofs, enabling decentralized and offline-capable authentication. To mitigate inferential privacy risks during data aggregation, DAPS integrates a modular ε-differential privacy mechanism based on the Laplace distribution, allowing configurable privacy-utility trade-offs for aggregated sensor data. The framework is evaluated through functional, performance, security, and privacy analyses. Functional evaluation verifies the correct realization of DID management, VC lifecycles, and authentication workflows. Performance analysis characterizes the behavior of critical operations under concurrent load, highlighting the impact of architectural choices such as synchronous and asynchronous blockchain interactions. Security evaluation assesses the system against an explicit threat model, examining resistance to impersonation, replay, and tampering within the assumed trust boundaries. Privacy evaluation empirically validates the behavior of the differential privacy mechanism, illustrating the trade-off between privacy guarantees and analytical utility. The results demonstrate how W3C-compliant decentralized authentication, integrated with differential privacy mechanisms, can be realized as a reference system on enterprise blockchain platforms without native SSI support, providing a reusable architectural and implementation blueprint for large-scale multi-agent environments.
Current approaches to verifying AI training data compliance face a fundamental tension: copyright holders need to know whether their content was used in training (EU AI Act, Article 53(1)(d)), while model providers need to protect their training data as trade secrets (GDPR, trade secret law). Existing zero-knowledge proof systems for machine learning (ZKML) address this partially by providing proofs of non-membership for exact data points. However, real-world training pipelines involve tokenization, chunking, paraphrasing, and augmentation, rendering exact-match proofs insufficient. We identify a gap in the literature: no existing system combines semantic fingerprinting with zero-knowledge proofs to enable semantic non-membership verification. We propose an architecture for Zero-Knowledge Semantic Non-Membership (ZK-SNM) that enables a model provider to prove, without revealing any training data, that no document in their training corpus is semantically similar to a queried document above a specified threshold. We discuss the technical challenges, including the computational cost of similarity search within ZK circuits, and propose mitigation strategies based on locality-sensitive hashing and hierarchical verification. This position paper establishes the problem formulation and proposed architecture; experimental validation is left to subsequent work.
Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.
GLYPH is a transparent verification layer for Ethereum for trustless on-chain verification of heterogeneous proof systems. It unifies upstream SNARK and STARK settlement through a single packed arity-8 sumcheck verifier over p = 2^128 - 159, while preserving upstream assumptions. The design centers on a universal adapter surface, UCIR compilation, and a chain-bound artifact interface for stateless verification. Benchmark evidence in the whitepaper reports 29.45k total transaction gas in recorded testnet receipts. This record includes the whitepaper and the formal proof appendix.
Pankaj Kumar, Arun K H, Yogesh N, Prakash Babu · 8 authors
The growing dependance on data-based decisionmaking in healthcare has brought attention to the vital importance of secure, privacy-preserving and collaborative learning techniques. Traditional centralized learning approaches in medical data often raise concerns regarding patient privacy data leaks and even regulatory troubles. Federated learning came as a good solution, where it allows model training in different hospitals without sharing the sensitive patient data. However, federated learning has its problems - it can be mislead with fake updates, the model can even be poisoned and it is really hard to trust every participants involved. In this work, we present fed-chain, a secure and scalable framework which brings together federated learning, blockchain and zero-knowledge-proofs(ZKPs) preserving the privacy of patient's data in healthcare. Blockchain here adds decentralized trust, immutability and makes model updates transparent to review while ZKPs helps in proving correctness without leaking personal data. We are implemented this framework for heart disease prediction where multiple hospitals train the model together but the data stays confidential. Our experimental results shown better accuracy, more strength against attacks and even low communication cost compared to other FL setups. Overall, the systems gives a safer approach for working together on healthcare data, allowing hospitals and research centers to generate valuable predictions using these models while keeping the patient data private and safe.
Mohammad Fairus Bin Zulkifli, Rabiah Abdul Kadir, mohamad nazir ahmad
Growing reliance on digital knowledge sharing across academic, corporate, and public sectors has raised serious concerns about data integrity, trust, and security. Blockchain consensus mecha-nisms offer a promising path forward through decentralized, transparent, and tamper-proof frameworks. This systematic review examines how these mechanisms enhance trust in knowledge sharing platforms, focusing on four directions: how these mechanisms are applied within knowledge sharing con-texts, the challenges they introduce for knowledge sharing de-ployment, and the advantages they provide to trust-based knowledge sharing ecosystems. Following PRISMA 2020 guide-lines, three databases Scopus, IEEE Xplore, and Web of Science were searched, and peer-reviewed studies published between 2020 and 2025 were selected for analysis. In terms of knowledge sharing applications, blockchain consensus mechanisms build trust through multiple co-occurring pathways, including distrib-uted verification, transparency, cryptographic security, immu-tability, incentive alignment, and smart contract automation. Algorithms such as Proof of Work, Proof of Stake, Delegated Proof of Stake, and Byzantine Fault Tolerance variants are widely adopted, each offering different trade-offs between secu-rity, efficiency, and scalability. In terms of challenges, scalabil-ity, energy consumption, and integration complexity with exist-ing systems remain the most significant barriers to adoption. In terms of advantages, blockchain consistently delivers stronger data security, greater transparency, and reduced dependence on centralized authorities across knowledge sharing contexts. This review concludes that blockchain consensus mechanisms offer layered and compounding trust benefits, yet technical and or-ganizational barriers continue to limit widespread deployment. Future research should focus on energy-efficient protocols, scalable architectures, and real-world effectiveness studies.