Blockchain-based Attribute-Based Access Control (BC-ABAC) offers a decentralized paradigm for secure data governance but faces two inherent challenges: the transparency of blockchain ledgers threatens user privacy by enabling reidentification attacks through attribute analysis, while the computational complexity of policy matching clashes with blockchain's performance constraints. Existing solutions, such as those employing Zero-Knowledge Proofs (ZKPs), often incur high overhead and lack measurable anonymity guarantees, while efficiency optimizations frequently ignore privacy implications. To address these dual challenges, this paper proposes QAEBAC (Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute). QAE-BAC introduces a formal (r, t)-anonymity model to dynamically quantify the re-identification risk of users based on their access attributes and history. Furthermore, it features an Entropy-Weighted Path Tree (EWPT) that optimizes policy structure based on realtime anonymity metrics, drastically reducing policy matching complexity. Implemented and evaluated on Hyperledger Fabric, QAE-BAC demonstrates a superior balance between privacy and performance. Experimental results show that it effectively mitigates re-identification risks and outperforms state-of-the-art baselines, achieving up to an 11x improvement in throughput and an 87% reduction in latency, proving its practicality for privacy-sensitive decentralized applications.
Driven by the increasing demand for multi-party data computation, Private Set Intersection (PSI) has become a pivotal technique for secure data sharing and privacy preservation. Although several efficient two-party PSI protocols have been developed, multi-party scenarios continue to suffer from limited computational efficiency and inadequate security guarantees. To address this engineering challenge, this study aims to enhance the performance and security of multi-party PSI protocols. We introduce SM-MPSI, a multi-party PSI protocol built upon national cryptographic standards. This protocol integrates SM2 and SM3 cryptographic mechanisms, employs non-interactive zero-knowledge proofs for identity authentication, and leverages domestic secure cryptographic chips to accelerate core algorithms. Experimental comparisons with existing mainstream protocols demonstrate significant improvements in computational efficiency and system scalability, while preserving robust security guarantees. Furthermore, SM-MPSI achieves enhanced communication efficiency and reduced resource consumption in multi-party scenarios. This research offers technical contributions toward advancing China's efforts in independent innovation in privacy-preserving computing and cryptographic technologies, thereby laying a solid foundation for strengthening national cybersecurity capabilities.
BrainâComputer Interfaces (BCIs) represent a transformative paradigm in humanâmachine interaction, enabling direct communication between neural signals and external devices. They hold immense promise in domains such as medical neuroprosthetics, defense communication, and immersive gaming. However, the neural data they process is highly sensitive, and current BCI frameworks that rely on centralized servers and traditional encryption are vulnerable to data breaches, manipulation, and the emerging threats of quantum decryption. These limitations highlight the urgent need for secure, privacy-preserving, and resilient architectures for BCI communication. To address these challenges, this paper introduces NeuroGuard, a blockchain-based framework enhanced with Post-Quantum Cryptography (PQC) algorithmsâCRYSTALS-Kyber for secure key exchange and Dilithium for digital signaturesâcombined with Zero-Knowledge Proofs (ZKPs) for lightweight device authentication. Neural data packets are logged in a decentralized ledger, ensuring immutability, transparency, and tamper-proof communication. A prototype system was implemented using an EEG-based BCI headset with edge preprocessing and blockchain-secured communication. Experimental results demonstrate a 31% improvement in attack resistance, 22% reduction in latency, and complete removal of central points of failure compared to traditional BCI security models. The novelty of NeuroGuard lies in integrating PQC, blockchain, ZKPs, and edge intelligence into a unified BCI security architecture, paving the way for future quantum-resilient neural communication systems.
This study presents a comprehensive framework that integrates deep learning and blockchain security to address key challenges in cryptocurrency forecasting and privacy preservation. A state-of-the-art ensemble machine learning model, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, is proposed for Bitcoin price prediction. The model achieves 92.1% accuracy on out-of-sample data following rigorous validation, demonstrating strong forecasting performance. To address fundamental security and privacy concerns in blockchain systems, a dynamic privacy framework is proposed, which integrates Zero-Knowledge Proofs (ZKPs) and adaptable consensus methods to improve transaction confidentiality, scalability, and adherence to regulations.
Traditional information system auditing faces severe challenges in data integrity verification and audit transparency. Manual sampling methods are not only inefficient but also vulnerable to data tampering attacks. This paper proposes a blockchain-based auditing framework that integrates SHA-256 hash chains, ECDSA digital signatures, and zero-knowledge proofs to establish a cryptographically secure and tamper-proof auditing environment. The framework employs a three-layer architecture: the data integrity layer uses hash chains to ensure audit records are immutable; the authentication layer uses digital signatures to verify the non-repudiation of evidence; and the privacy layer implements zero-knowledge proofs to protect sensitive data. To verify the frameworkâs effectiveness, a comprehensive experiment was conducted on a private Ethereum network with five verification nodes, processing 10,000 to 100,000 audit records. The experimental results show a significant performance improvement. Efficiency is improved by $65 \%$, data processing throughput reaches 500 records/second with a response latency of less than 2 seconds, and the average time for hash calculation and digital signature verification is 0.8 milliseconds and 1.2 milliseconds, respectively. Data integrity verification efficiency is improved by $78 \%$ compared with traditional methods, and reliability reaches $99.99 \%$. Comparative experiments show that compared with traditional database-centric auditing systems, the proposed system improves processing throughput by $178 \%$, and reduces manual reconciliation time from 2.5 hours to near real-time. This solution provides a practical, efficient and scalable method for auditing next-generation information systems in enterprise environments.
In edge computing, fusing privacy-sensitive heterogeneous sensor data poses challenges in balancing utility, privacy, and efficiency. Existing approaches like zkFL and zkGPT fall short in end-to-end verifiable LLM-driven fusion for non-IID data. We propose a framework embedding ZKPs into adaptive LLM layers for secure multimodal fusion with provable privacy. Key contributions: (1) context-aware attention for LLM fusion; (2) custom zk-SNARK circuits for full verification; (3) dynamic edge optimizations reducing latency by $\mathbf{2 5} \boldsymbol{\%}$. Theoretical analyses provide -DP bounds and convergence guarantees. Experiments on UCI HAR and CIFAR extensions show 91.8% accuracy, MI-AUC of 0.52, and $\mathbf{4 5 ~ m s}$ latency on Jetson Nano, outperforming zkFL by 3.5% in accuracy and 25% in efficiency.
P Arockia Mary, Saranya R, Dharanitha S, Dhanya S · 6 authors
In the contemporary democratic scenario, the secure, transparent, and trustworthy nature of digital elections is of ultimate importance. This objective is achieved via the innovative approach of Hybrid Proof-of-Stake (PoS) and Zero-Knowledge Proof (ZKP) based E-Voting System, which enhances integrity and overcomes challenges faced by the existing models. In contrast to the classical Proof-of-Authority (PoA) model, in which validators are pre-approved, the Hybrid PoS within this system decentralizes the selection of validators by the participation of all network members. It mitigates the risks of centralization and ensures fairness. The voting begins with Voter Registration, wherein Decentralized Identity (DID) establishes that the voter is eligible without revealing any relevant identity information. Through ZKP, voters authenticate their eligibility to vote and, further, acquire a voting token through a smart contract, granting them possible access to the election. Validators are selected dynamically on the basis of stake contributions in that PoS mode to ensure periodic rotation among validators to avoid collusion. In the Vote Casting Phase, ZKP encryption ensures the secrecy of the vote while allowing for its independent verification. Votes are stored immutably on the blockchain, transaction hashes being made available for the independent verification of individual votes. The Vote Validation Phase ensures that PoS-selected validators authenticate the votes and prevent double voting while enforcing the election rules to minimize manipulation risk. Finally, during the Vote Tallying & Result Declaration Phase, the smart contract tallies results and records the information permanently on a blockchain for the purposes of both transparency and protection. This system integrates Hybrid PoS for secure selection of validators and ZKP for privacy-protecting authentication, forming a scalable, fraud-resistant and verifiable e-voting framework.
This paper reconstructs zero-knowledge extensions on Solana as an architecture theory. Drawing on the existing ecosystem and on the author's prior papers and implementations as reference material, we propose a two-axis model that normalizes zero-knowledge (ZK) use by purpose (scalability vs. privacy) and by placement (on-chain vs. off-chain). On this grid we define five layer-crossing invariants: origin authenticity, replay-safety, finality alignment, parameter binding, and private consumption, which serve as a common vocabulary for reasoning about correctness across modules and chains. The framework covers the Solana Foundation's three pillars (ZK Compression, Confidential Transfer, light clients/bridges) together with surrounding components (Light Protocol/Helius, Succinct SP1, RISC Zero, Wormhole, Tinydancer, Arcium). From the theory we derive two design abstractions - Proof-Carrying Message (PCM) and a Verifier Router Interface - and a cross-chain counterpart, Proof-Carrying Interchain Message (PCIM), indicating concrete avenues for extending the three pillars.
The conventional voting systems are flawed in terms of integrity and security since they lack the challenge of public transparency and trust. The potential of blockchain technology lies in the ability of the technology to guarantee data integrity, decentralize the procedures via diverse networks, and certify sophisticated security services. The paper delves into the application of blockchain in election processes through the analysis of the performance of blockchain to provide secure, tamper-free and transparent voting systems. The introductory section gives some background of the voting process which requires a firmer mechanism and anti-fraud evidence in the same. Part two of this work describes what the literature on the topic of research has to say about voting solutions that are based on blockchain technology. The paper describes the Proof of Work (PoW) protocol, Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) protocols. It examines encrypted data techniques in Zero-Knowledge Proofs (ZKPs) and cryptography AES and RSA. The paper also expounds the compatibility of smart contracts with automated voter verification systems and dealing with ballot collection and release of results. This paper will explain blockchain electorate architecture and its key challenges to implementation which include scalability problem, rules and access. The article mentions future of blockchain application in elections to show how blockchain technology application may make the election process more open and create a more trustful voter base. With this review, it is possible to understand how blockchain can be used to make elections more transparent and make citizens trust the democratic process.
The Internet of Medical Things (IoMT) transforms healthcare by enabling real-time monitoring of patient vitals, such as heart rate and glucose levels, but faces significant challenges in securing sensitive data against cyber threats and ensuring reliability in resource-constrained wearable devices, like low-power biosensors with limited computational capacity. The rise of quantum computing, particularly Shor algorithm, threatens to break traditional cryptographic methods (e.g., RSA, ECC) within 5â10 years by efficiently solving their underlying mathematical problems, endangering patient data confidentiality. Post-quantum cryptography (PQC), such as lattice-based schemes, offers resilience but demands high computational resources, challenging IoMT scalability. Unlike other PQC IoMT frameworks, such as those using NTRU, which prioritize computational simplicity but lack advanced privacy mechanisms, Q-PRADAX pioneers a secure, adaptive data aggregation framework, integrating Ring-LWE-based PQC for quantum-resilient confidentiality, compact zk-SNARK proofs for tamper-proof verification of patient vitals, and adaptive clustering for enhanced network reliability and scalability. Evaluated using OMNeT + + 6.0.3 with INET 4.5, Q-PRADAX achieves 94.5% diagnostic accuracy on ECG datasets, 100% tampering detection, and 99.9% packet delivery across 1000 devices in its Baseline scenario, with a security latency of 12.2 ms/packet and energy consumption of 0.38 mJ/packet on ARM Cortex-M4 devices (200 mAh). Outperforming existing IoMT solutions in security and fault tolerance, Q-PRADAX establishes a global standard for a secure, patient-centric IoMT ecosystem, redefining reliable healthcare delivery.
As healthcare ecosystems shift toward digital-first operations, personal health data faces unprecedented security and privacy risks from increasingly sophisticated cyber threats. This paper examines how the integration of Artificial Intelligence (AI), including Agentic AI, blockchain, and cloud computing, can establish an advanced security framework for resilient healthcare data management. Unlike traditional siloed systems, the proposed model leverages AI-driven anomaly detection, multi-agent orchestration, and explainable AI (XAI) for real-time threat prediction and adaptive defense. Blockchain contributes decentralized trust, tamper-proof auditability, and consent-enforcing smart contracts, while cloud platforms deliver elastic scalability, encrypted storage, and hybrid multi-cloud deployment models. The framework also incorporates federated learning, Model-Chaining Protocols (MCPs), and Zero-Knowledge Proofs (ZKPs) to enhance interoperability, preserve privacy, and enable verifiable compliance. Findings highlight significant improvements in confidentiality, integrity, and availability (CIA) of healthcare data, while simultaneously addressing regulatory obligations such as HIPAA and GDPR through embedded governance and risk orchestration layers. Despite challenges around system complexity and policy harmonization, the paper provides a state-of-the-art synthesis and proposes actionable best practices for healthcare practitioners and policymakers, including adopting continuous AI-powered risk monitoring, blockchain-based patient-centric data ownership, and automated compliance verification mechanisms. Overall, the convergence of AI, blockchain, and cloud technologiesâaugmented by governance-driven orchestrationâoffers a future-proof, cyber-resilient architecture for safeguarding personal health data in digital-first healthcare ecosystems.
Digital transactions currently exceed trillions of dollars annually, yet traditional paper-based agreements remain a bottleneck for automation, enforceability, and dispute resolution. Natural language contracts introduce ambiguity, require manual processing, and lack computational verifiability, all of which hinder efficient digital commerce. Computable legal contracts, expressed in machine-readable formats, offer a potential solution by enabling automated execution and verification. Blockchain-based smart contracts further strengthen enforceability and accelerate dispute resolution; however, current implementations risk exposing sensitive agreement terms on public ledgers, raising serious privacy and competitive intelligence concerns that limit enterprise adoption. We introduce zk-agreements, a protocol designed to transition from paper-based trust to cryptographic trust while preserving confidentiality. Our design combines zero-knowledge proofs to protect private agreement terms, secure two-party computation to enable private compliance evaluation, and smart contracts to guarantee automated enforcement. Together, these components achieve both privacy preservation and computational enforceability, resolving the fundamental tension between transparency and confidentiality in blockchain-based agreements.
This paper addresses the challenge of designing secure and private digital credentialing systems by leveraging advanced mathematical primitives from applied cryptography. The core of our proposed solution is the application of Zero-Knowledge Proofs (ZKPs), a class of cryptographic protocols that allows for the verification of assertions without disclosing the underlying secret data. We introduce a formal, layered architecture that demonstrates how the mathematical properties of ZKPs can be systematically translated into a robust, large-scale information system. The framework's design is validated against the complex requirements of the academic domain, which serves as a rigorous testbed for our architectural approach. The primary contribution is a blueprint for integrating complex cryptographic protocols into practical system design, demonstrating how mathematical guarantees of privacy can be preserved in a distributed and verifiable manner. This work provides a novel contribution at the intersection of applied cryptography, system architecture, and information security.
Saud Sultan Al Qasimi, Qassim Nasir, Manar Abu Talib
Secure, trustworthy electronic elections require four key properties: verifiable eligibility, strong ballot privacy, revoting for coercion resistance, and nation-scale throughput. Existing blockchain-based prototypes address these goals only in fragments, while verified credential to Zero Knowledge Proof (ZKP) conversion is ad hoc, re-voting schemes leak timing data or exhaust gas limits, and âtrack-your-voteâ features issue receipts that a coercer can demand. ZKP-E-Vote closes these gaps with a fully specified architecture. A compulsory UAE-Pass login feeds a Groth16 circuit that converts government credentials into a non-linkable pseudonym$P$and a zero-knowledge eligibility proof. Ballots embed a monotone counter and a hash-derived nullifier, enabling any voter to overwrite a coerced ballot while revealing nothing regarding their re-vote. After polls close, threshold-decryption verification rooms let citizens confirm their own ballots without exporting a receipt; while a public zk-proof concurrently attests that every tallied ciphertext is the latest for its pseudonym and that the homomorphic total matches the announced result. All proofs are generated off-chain and recursively aggregated; relay nodes batch thousands of ballots into roll-up blocks, fixing onchain state at one 32-byte slot per voter, and bounding verification to a single pairing check per batch. The framework thus unites eligibility privacy, coercion-safe revoting, receipt-free voter assurance, and national-scale feasibility in a single auditable design, offering a concrete roadmap toward a pilot-ready blockchain election system.
IvĂĄn AbellĂĄn Ălvarez, Pol Hölzmer, Johannes Sedlmeir
Digital identity wallets promise significant advancements in digital identity management by offering users a high degree of convenience, security, and control over their data disclosure. However, there is also criticism regarding their privacy guarantees, especially when used in regulated use cases that require high levels of assurance on the correctness and binding of a legal identity. In this paper, we present a comprehensive privacy model and analysis of one of the most prominent digital wallets â the European Digital Identity Wallet (EUDIW) â as specified by the Architecture and Reference Framework (ARF) and the eIDAS 2.0 regulation. We employ a suite of qualitative privacy risk assessment methods to systematically map and evaluate information flows in three key use cases. Our analysis identifies multiple privacy risks â including linkability, identifiability, and excessive attribute data disclosure â and reveals that although the ARF is designed to comply with privacy-by-design principles, inherent design choices, such as the reliance on SD-JWT and mDOC data formats, as well as the concept of a Wallet Unit Attestation (WUA), retain risks to user privacy. Building on our findings, we then highlight how advanced Privacy-Enhancing Technologies (PETs), such as (general-purpose) Zero-Knowledge Proofs (ZKPs), can reduce or mitigate some of these risks.
The integrity, coupled with the transparency of electoral systems, is vital for the existence of a âdemocratic society if that society is to function well. Often, conventional electronic voting mechanisms âare criticized for their security vulnerabilities, with a lack of transparency, together with limited public trust. âBlockchain technology has come about to be a possible enabler for trustless and immutable âsystems. However, such a standard, privacy-preserving, verifiable voting model remains elusive. This âwork seeks to fill this void with the use of a blockchain e-voting system that uses QR codes to âvalidate voters, cryptographically ensures integrity with the EFFT-SWIFFT hash, and also handles âballots through smart contracts. A feature matrix together with a visual chart was used in a systematic âliterature review of 28 peer-reviewed papers to analyze and compare authentication methods, âtransparency techniques, consensus mechanisms, and scalability solutions. Though the analysis reveals âthat entities greatly underutilize advanced cryptographic primitives such as zero-knowledge proofs and âpost-quantum hashing, these primitives potentially improve privacy and also verifiability. Present in the âproposed model is a multi-layered architecture. Also, the model can offer a secure as well as âtransparent solution for addressing these gaps. Blockchain-based e-voting can increase trust, reduce âfraud, and broaden democratic participation, but it requires real-world validation through pilot projects âand usability testingâ.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Hasan Akgul, Daniel Borg, Arta Berisha, Amina Rahimova · 6 authors
Large language models are often adapted through parameter efficient fine tuning, but current release practices provide weak assurances about what data were used and how updates were computed. We present Verifiable Fine Tuning, a protocol and system that produces succinct zero knowledge proofs that a released model was obtained from a public initialization under a declared training program and an auditable dataset commitment. The approach combines five elements. First, commitments that bind data sources, preprocessing, licenses, and per epoch quota counters to a manifest. Second, a verifiable sampler that supports public replayable and private index hiding batch selection. Third, update circuits restricted to parameter efficient fine tuning that enforce AdamW style optimizer semantics and proof friendly approximations with explicit error budgets. Fourth, recursive aggregation that folds per step proofs into per epoch and end to end certificates with millisecond verification. Fifth, provenance binding and optional trusted execution property cards that attest code identity and constants. On English and bilingual instruction mixtures, the method maintains utility within tight budgets while achieving practical proof performance. Policy quotas are enforced with zero violations, and private sampling windows show no measurable index leakage. Federated experiments demonstrate that the system composes with probabilistic audits and bandwidth constraints. These results indicate that end to end verifiable fine tuning is feasible today for real parameter efficient pipelines, closing a critical trust gap for regulated and decentralized deployments.
Recent advances in lattice-based cryptography have seen Chen et al. introduce a linkable ring signature scheme (LLRS) for cloud-assisted electronic medical record systems, claiming dual security properties of linkability and unforgeability (IEEE Trans. Inf. Forensics Secur., doi: 10.1109/TIFS.2024.3455772). Our cryptanalysis reveals critical security flaws in their construction: the scheme fails to satisfy either claimed property. Specifically, we demonstrate universal forgeability through adaptive message attacks and identify defective linkability verification that permits signature origin ambiguity. Following vulnerability demonstrations via concrete attack vectors, we trace these weaknesses to flawed parameter initialization and improper nonce handling in their zero-knowledge proof framework. We conclude with concrete mitigation strategies including strengthened commitment schemes and improved randomness management.