Maninder Singh, William Bjorndahl, Gagangeet Singh Aujla, Joseph Camp
In the era of continuously increasing demand for bandwidth and revolutionary wireless technologies, efficient spectrum management is essential. This paper proposes a novel multi-tier tokenization approach for dynamic spectrum management. Leveraging the concept of heterogeneous tokenization of spectrum bands, we develop a decentralized framework based on blockchain technology that enables the sharing of spectrum among users. The spectrum space is represented by multi-planes, the first plane consists of unique spectrum bands converted into NFTs for long-term allocations, while the second plane involves subdividing these NFT spectrum bands for short-term usage by retail users through fungible tokens. The fungible tokens are dynamically traded and mapped using particle swarm optimization (PSO) to manage demand and supply. The paper presents formal models of the involved entities and algorithms for creating multi-tier tokens, dynamic token trading and demand-supply mapping using PSO. To enhance privacy, a zero-knowledge proof (ZKP) based approach is employed for user authentication. The proposed framework offers a secure, transparent, and scalable solution for spectrum management, addressing the limitations of traditional centralized approaches. Simulation results demonstrate the effectiveness of the framework in dynamic spectrum access, while providing privacy-aware and scalable solutions suitable for future wireless networks, including 6G.
With the rise of machine learning techniques, ensuring the fairness of decisions made by machine learning algorithms has become of great importance in critical applications. However, measuring fairness often requires full access to the model parameters, which compromises the confidentiality of the models. In this paper, we propose a solution using zero-knowledge proofs, which allows the model owner to convince the public that a machine learning model is fair while preserving the secrecy of the model. To circumvent the efficiency barrier of naively proving machine learning inferences in zero-knowledge, our key innovation is a new approach to measure fairness only with model parameters and some aggregated information of the input, but not on any specific dataset. To achieve this goal, we derive new bounds for the fairness of logistic regression and deep neural network models that are tighter and better reflecting the fairness compared to prior work. Moreover, we develop efficient zero-knowledge proof protocols for common computations involved in measuring fairness, including the spectral norm of matrices, maximum, absolute value, and fixed-point arithmetic. We have fully implemented our system, FairZK, that proves machine learning fairness in zero-knowledge. Experimental results show that FairZK is significantly faster than the naive approach and an existing scheme that use zero-knowledge inferences as a subroutine. The prover time is improved by 3.1x--1789x depending on the size of the model and the dataset. FairZK can scale to a large model with 47 million parameters for the first time, and generates a proof for its fairness in 343 seconds. This is estimated to be 4 orders of magnitude faster than existing schemes, which only scale to small models with hundreds to thousands of parameters.
This paper presents an implementation of a Self-Sovereign Identity (SSI) framework using Ethereum-based standards to meet the technical requirements of the European Digital Identity (EUDI) Architecture Reference Framework (ARF). By leveraging ERC-734/ERC-735 standards, the proposed eSSI system enables decentralized key management, verifiable claims, and onchain auditability. A case study on the Sepolia testnet demonstrates functional alignment with EUDI goals, while highlighting the need for enhanced privacy mechanisms such as zero-knowledge proofs for full compliance.
Objective: The combination of 5G connectivity and edge computing known as 5G Edge, lifts the limitations of the Internet of Medical Things (IoMT) and enables a plethora of authentic healthcare services, including access to medical information and diagnosis. Additionally, there is a chance that malicious insiders using the 5G Edge platform could compromise the security and confidentiality of IoMT data. As a result, end users cannot trust 5G Edge data. Materials and methods: This paper imagines a new hierarchical blockchain edge of things (HBEoT) architecture that would facilitate healthcare applications managed by blockchain at the network edge. Additionally, the article delves into how HBEoT can offer security services such as authenticating users, protecting data, detecting attacks, and managing trust. First, the consensus method PoS generates a block for healthcare data, and its hash values are kept in the blockchain. The Zero Knowledge Proof (ZKP) protocol stores secret information in the blockchain, authenticating healthcare systems for enhanced privacy and security. Results: To ensure immutable data storage, the suggested architecture incorporates 5G Edge servers into a blockchain platform. It prevents unauthorised users from accessing healthcare services by acting as an anonymous authenticator. We assessing the proposed methods efficiency in terms of latency, throughput, communication cost, energy consumption cost and the validation between ZKP prover and verifier. Conclusion: We conduct multiple experiments to evaluate the effectiveness of the suggested framework. To further investigate the quality measures, such as authenticate latency, throughput rate was examined. To fulfil the requirements for a 5G-enabled healthcare system, the research shows that the suggested HBEoT-ZKP performs a decrease in latency of around 1.16 s and an improvement in throughput of approximately 339 tps. Communication and energy cost is evaluated and compared with other conventional methods.
Cryptography has played a pivotal role in securing communication across human history. From ancient techniques such as hieroglyphic substitutions and Caesar's cipher to contemporary cryptographic systems like RSA and Elliptic Curve Cryptography, the field has continuously adapted to evolving technological paradigms. This article provides a comprehensive review of the historical development of cryptography, highlighting key milestones from ancient Egypt and Mesopotamia, through the mechanical encryption devices of World War II, to the theoretical foundations established by Claude Shannon. It examines the revolutionary introduction of public-key cryptography and follows developments into the digital era, where blockchain technology and privacy innovations like Zero-Knowledge Proofs have expanded cryptographic applications beyond traditional security roles. The article also explores emerging challenges and innovations, particularly those involving artificial intelligence and quantum computing, considering the implications of quantum threats and the ongoing global efforts to develop quantum-resistant encryption standards.
Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks.Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements.Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures.To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method.Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure.Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16KB or more, outperforming state-of-the-art methods. CCS Concepts Security and privacy File system security; Management and querying of encrypted data.
Critical peer review of scientific manuscripts presents a significant challenge for Large Language Models (LLMs), partly due to data limitations and the complexity of expert reasoning. This report introduces Persistent Workflow Prompting (PWP), a potentially broadly applicable prompt engineering methodology designed to bridge this gap using standard LLM chat interfaces (zero-code, no APIs). We present a proof-of-concept PWP prompt for the critical analysis of experimental chemistry manuscripts, featuring a hierarchical, modular architecture (structured via Markdown) that defines detailed analysis workflows. We develop this PWP prompt through iterative application of meta-prompting techniques and meta-reasoning aimed at systematically codifying expert review workflows, including tacit knowledge. Submitted once at the start of a session, this PWP prompt equips the LLM with persistent workflows triggered by subsequent queries, guiding modern reasoning LLMs through systematic, multimodal evaluations. Demonstrations show the PWP-guided LLM identifying major methodological flaws in a test case while mitigating LLM input bias and performing complex tasks, including distinguishing claims from evidence, integrating text/photo/figure analysis to infer parameters, executing quantitative feasibility checks, comparing estimates against claims, and assessing a priori plausibility. To ensure transparency and facilitate replication, we provide full prompts, detailed demonstration analyses, and logs of interactive chats as supplementary resources. Beyond the specific application, this work offers insights into the meta-development process itself, highlighting the potential of PWP, informed by detailed workflow formalization, to enable sophisticated analysis using readily available LLMs for complex scientific tasks.
As supply chains become increasingly digitized and decentralized, ensuring security, traceability, and data integrity has emerged as a critical concern. Blockchain technology has shown significant potential to address these challenges by providing immutable records, transparent data flows, and tamper-resistant transaction logs. However, the effective application of blockchain in real-world supply chains requires the careful evaluation of both architectural design and technical limitations, including scalability, interoperability, and privacy. This review systematically examines existing blockchain-based supply chain solutions, classifying them based on their structural models, cryptographic foundations, and storage strategies. Special attention is also given to underexplored humanitarian logistics scenarios. It introduces a three-dimensional evaluation framework to assess security, traceability, and integrity across different architectural approaches. In doing so, it explores key technological enablers, including advanced mechanisms such as zero-knowledge proofs (ZKPs) and cross-chain architectures, to meet evolving privacy and interoperability demands. Furthermore, this study outlines a conceptual cross-chain interaction scenario involving permissioned and permissionless blockchain networks, connected through a bridge mechanism and supported by representative smart contract logic. The model illustrates how decentralized stakeholders can interact securely across heterogeneous blockchain platforms. By integrating quantitative metrics, architectural simulations, and qualitative analyses, this paper contributes to a deeper understanding of blockchain’s role in next-generation supply chains, offering guidance for researchers and practitioners aiming to design resilient and trustworthy supply chain management (SCM) systems.
Beal's Conjecture (Andrew Beal, 1993) is a Clay Mathematics Institute Millennium Prize problem. It generalizes Fermat's Last Theorem: the exponential Diophantine equation Ax + By = Cz with positive integers A, B, C, x, y, z and x, y, z > 2 has integer solutions only if A, B, C share a common prime factor. This manuscript presents a conditional resolution via the Harmonic Coherence (HC) framework and Hanners Theorem (HT). We translate entropy-minimization principles from HC and HT into number theory. An entropy functional H(A,B,C) is defined over the normalized terms (Ax, By, Cz). Equilibrium (gradient zero) yields pi = 1/3, which requires Ax = By = Cz. Combined with Ax + By = Cz, this implies 2Cz = Cz—impossible for positive integers. Thus no coprime solution can satisfy equilibrium; any admissible solution must share a common prime factor. The proof is supported by modular arithmetic and congruence arguments (Stewart–Tijdeman, Darmon–Granville) and by extensive computational validation (34 tests, all PASS) over large integer domains. No counterexamples were found. v5.0 changes: Fixed L1 displacement bound in Lemma coprime-displacement from ≥ 1/6 to the correctly derived ≥ 1/3. Added perturbative translation note to the A3 closure strategy (bounded-height families as amplitude cutoff). Updated documentation (test count 15 → 34, Zenodo DOI). All changes sourced from deep vector DB mining of the knowledge system. Companion documents: • Contextual Entropy Reduction Theorem • Canonical Reconciliation (Song of Coherence) • HC Bridge Note • Fixed-Point Convergence Theorem • Paper A: Transformer Distillation as Spectral Filtering • Paper B: GW Kerr Ringdown • Paper C: HC Bridge Synthesis
Mohammed Ibraheem Hussein, Ohood Saadoon Hlail, Asma Ibrahim Hussein, Amjed Abbas Ahmed · 6 authors
Balancing efficient threat detection with data privacy becomes increasingly difficult as cyber threats develop in complexity. The Adaptive Zero-Knowledge Threat Hunting Framework (AZTH), a revolutionary integration of zero-knowledge proofs (ZKP) and artificial intelligence (AI) for private and secure cybersecurity operations, is presented in the presented study. AZTH maintains strong confidentiality regarding sensitive data yet uses federated learning, quantum-resistant cryptography, and dynamic deception systems to improve threat intelligence sharing as well as real-time threat mitigation. Together with an assessment of its efficacy in several operating situations, the architecture, approach, and possible uses of the framework are given.
This article explores the complex intersection of technological innovation and privacy considerations in cloud identity management systems. It traces the evolution from traditional authentication methods to sophisticated cloud-based frameworks that now incorporate adaptive authentication, federation protocols, biometric verification, and behavioral analytics. While these advances enhance security and user experience, they simultaneously introduce profound privacy challenges regarding data collection scope, user profiling, and cross-platform tracking. The article examines how regulatory frameworks, industry self-regulation, and stakeholder perspectives shape the governance landscape of digital identity. Drawing on interdisciplinary research, the article reveals how trust formation in digital environments correlates with transparency practices and how trust erosion carries consequences beyond immediate user relationships. Looking forward, emerging technologies like decentralized identity and zero-knowledge proofs offer promising privacy-preserving alternatives, while evolving market dynamics and user expectations create both challenges and opportunities. The article concludes with actionable recommendations for designing and implementing identity systems that achieve an optimal balance between robust security, operational efficiency, and respect for individual privacy rights—a critical imperative for sustainable digital ecosystems in increasingly connected societies.
The rapid growth of digital technologies has intensified concerns about data privacy and security. Blockchain technology, combined with advanced cryptographic methods, presents a promising solution to enhance digital privacy by enabling decentralized, transparent, and tamper-resistant systems. This article explores the foundational principles of blockchain and cryptography, evaluates their interplay in preserving digital privacy, and examines current challenges and future directions. Through comprehensive analysis, the study highlights how cryptographic protocols such as zero-knowledge proofs and homomorphic encryption can significantly augment privacy on blockchain platforms, while addressing inherent scalability and usability challenges.
Although differential privacy (DP) is widely regarded as the de facto standard for data privacy, its implementation remains vulnerable to unfaithful execution by servers, particularly in distributed settings. In such cases, servers may sample noise from incorrect distributions or generate correlated noise while appearing to follow established protocols. This work addresses these malicious behaviours in a distributed client-server-verifier setup, under Verifiable Distributed Differential Privacy (VDDP), a novel framework for the verifiable execution of distributed DP mechanisms. We systematically capture end-to-end security and privacy guarantees against potentially colluding adversarial behaviours of clients, servers, and verifiers by characterizing the connections and distinctions between VDDP and zero-knowledge proofs (ZKPs). We develop three novel and efficient instantiations of VDDP: (1) the Verifiable Distributed Discrete Laplace Mechanism (VDDLM), which achieves up to a 400,000x improvement in proof generation efficiency with only 0.1--0.2x error compared with the previous state-of-the-art verifiable differentially private mechanism and includes a tight privacy analysis that accounts for all additional privacy losses due to numerical imprecisions, applicable to other secure computation protocols for DP mechanisms based on cryptography; (2) the Verifiable Distributed Discrete Gaussian Mechanism (VDDGM), an extension of VDDLM that incurs limited overhead in real-world applications; and (3) an improved solution to Verifiable Randomized Response (VRR) under local DP, as a special case of VDDP, achieving up to a 5,000x reduction in communication costs and verifier overhead.
Don Roosan, Rubayat Khan, Saif Nirzhor, Fahmida Hai
Objective: The authors propose and evaluate a novel cybersecurity architecture for telehealth that is resilient against future quantum computing cyber threats. By integrating post-quantum cryptography (PQC) with quantum key distribution (QKD) and privacy-preserving mechanisms, data confidentiality and immutability for patient records in a post-quantum era are ensured. Methods: A multi-layered design approach was adopted. The PQC algorithms (e.g. CRYSTALS-Dilithium) were integrated at the blockchain consensus layer to resist quantum attacks. A directed acyclic graph (DAG)-based ledger managed high transaction throughput and latency constraints typical of telehealth. A QKD-enhanced key management protocol leveraged quantum channels for secure exchanges. Zero-knowledge proofs (ZKPs) and secure multiparty computation (MPC) verified transactions without exposing sensitive patient data. A granular access control model used attribute-based encryption and smart contracts to govern which participants could view or modify encrypted medical records. Results: The prototype was developed within a simulated telehealth network comprising hospitals, clinics, and patient devices. The PQC signatures at the consensus layer provided effective resistance to both classical and anticipated quantum attacks. The QKD facilitated secure key distribution, while ZKPs and MPC enabled validation of healthcare transactions without compromising patient privacy. Despite increased computational overhead, the DAG approach efficiently handled parallel transactions, indicating improved scalability compared to traditional linear blockchains. Conclusion: A QKD-enhanced, PQC-driven framework successfully addresses critical security and privacy requirements, safeguarding medical data from emerging quantum threats. Although overhead and infrastructural costs are significant, sustained cryptographic resilience and robust patient confidentiality underscore its suitability for next-generation healthcare systems. Future studies should explore additional optimizations, homomorphic encryption, and larger-scale pilots under regulatory standards.
Saad Alahmari, Amal Alshardan, Fahd N. Al‐Wesabi, Shaymaa E. Sorour · 8 authors
As healthcare services have become increasingly digitized, Electronic Health Records (EHRs) have become widely adopted, providing seamless data exchange among providers. Conventional EHRs, however, are extremely vulnerable to cyber threats because patients' sensitive data is centralized and transmitted electronically. The paper proposes a decentralized, privacy-preserving framework for managing EHRs on blockchains in order to address these security and privacy concerns. Using cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, the proposed system enhances security and ensures data integrity. Additionally, the model facilitates scalable, efficient, and secure access to patient records through the integration of cloud-based storage and blockchain. Using smart contracts, we also ensure compliance with healthcare regulations by regulating access control and authentication. As a result of performance evaluations, the proposed approach is demonstrated to be feasible, and the advantages it offers in terms of security, privacy, and efficiency are highlighted.
Julio López Fenner, Carlos Castillo-Muñoz, Francisco Escobar, Ana Bustamante-Mora · 5 authors
Privacy-preserving secure multi-party computation protocols are known to face scalability and efficiency challenges in environments where participants hold distinct attributes of the same records (vertical partitioning) or controls a subset of complete records (horizontal partitioning), as in cross-institutional health data analysis or federated IoT analytics, mostly because of communication overhead and the need to address adaptability to large scale or heterogeneous settings. This work introduces a novel MPC protocol based on the Damgård–Jurik cryptosystem and Schnorr zero-knowledge proofs (ZKP), designed to securely aggregate private data distributed across a number of parties. By combining homomorphic encryption with non-interactive ZKP’s, the protocol ensures privacy, correctness, and scalability, aligning with the principles of privacy-enhancing technologies (PETs). Our approach minimizes data exposure, allowing participants to audit results, and achieves linear O(N) communication complexity, thus making it suitable for large-scale applications in secure data analytics and collaborative computing.
Tarek Galal, Valeria Tisch, Katja Assaf, Andreas Polze
Railways provide a critical service and operate under strict regulatory frameworks for implementing changes or upgrades. Despite their impact on the public, these frameworks do not define means or mechanisms for transparency towards the public, leading to reduced trust and complex tracking processes. We analyse the German guideline for railway-infrastructural modifications from proposal to approval, using the guideline as a motivating example for modelling decisions in processes using digital signatures and zero-knowledge proofs. Therein, a verifier can verify that a process was executed correctly by the involved parties and according to specification without learning confidential information such as trade secrets or identities of the participants. We validate our system by applying it to the railway process, demonstrating how it realises various rules, and we evaluate its scalability with increased process complexities. Our solution is not railway-specific but also applicable to other contexts, helping leverage zero-knowledge proofs for public transparency and trust.
Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and (potentially sensitive or private) training data. So-called Zero-knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) would appear to provide the capability to verify model inference without access to such sensitive data. However, applying ZK-SNARKs to modern neural networks, such as transformers and large vision models, introduces significant computational overhead. We present TeleSparse, a ZK-friendly post-processing mechanisms to produce practical solutions to this problem. TeleSparse tackles two fundamental challenges inherent in applying ZK-SNARKs to modern neural networks: (1) Reducing circuit constraints: Over-parameterized models result in numerous constraints for ZK-SNARK verification, driving up memory and proof generation costs. We address this by applying sparsification to neural network models, enhancing proof efficiency without compromising accuracy or security. (2) Minimizing the size of lookup tables required for non-linear functions, by optimizing activation ranges through neural teleportation, a novel adaptation for narrowing activation functions' range. TeleSparse reduces prover memory usage by 67% and proof generation time by 46% on the same model, with an accuracy trade-off of approximately 1%. We implement our framework using the Halo2 proving system and demonstrate its effectiveness across multiple architectures (Vision-transformer, ResNet, MobileNet) and datasets (ImageNet,CIFAR-10,CIFAR-100). This work opens new directions for ZK-friendly model design, moving toward scalable, resource-efficient verifiable deep learning.
The integration of machine learning (ML) in healthcare has unlocked transformative potential in disease prediction, personalized treatment, medical imaging, remote patient monitoring, and genomic data analysis. However, the sensitive nature of medical data introduces critical concerns regarding patient privacy, data security, and regulatory compliance. This chapter presents a comprehensive overview of privacy-preserving machine learning approaches tailored for healthcare applications, with a focus on technical frameworks, real-time implementations, and regulatory alignment. It explores the use of advanced techniques such as federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs to safeguard patient information while maintaining model utility. The chapter also addresses domain-specific challenges in processing real-time health data streams and implementing privacy-aware algorithms in resource-constrained environments. By bridging the gap between technical innovation and clinical applicability, this work emphasizes the importance of secure, scalable, and ethically aligned ML solutions in modern healthcare ecosystems. The discussion was contextualized within current legal frameworks and highlights future directions for research and implementation to ensure trust, transparency, and resilience in data-driven medical systems.
Threshold multi-party fully homomorphic encryption (TMFHE) schemes enable efficient computation to be performed on sensitive data while maintaining privacy. These schemes allow a subset of parties to perform threshold decryption of evaluation results via a distributed protocol without the need for a trusted dealer, and provide a degree of fault tolerance against a set of corrupted parties. However, existing TMFHE schemes can only provide correctness and security against honest-but-curious parties. We construct a compact TMFHE scheme based on the Learning with Errors (LWE) problem. The scheme applies Shamir secret sharing and share resharing to support an arbitrary t-out-of-N threshold access structure, and enables non-interactive reconstruction of secret key shares using additive shares derived from the current set of online participants. Furthermore, the scheme implements commitment and non-interactive zero-knowledge (NIZK) proof techniques to verify the TMFHE operations. Finally, our experiments demonstrate that the proposed scheme achieves active security against malicious adversaries. It overcomes the limitation of existing TMFHE schemes that can only guarantee correct computation under passive semi-honest adversaries.
Fernando Castillo, Jonathan Heiss, Sebastian Werner, Stefan Tai
Blockchain and distributed ledger technologies (DLTs) facilitate decentralized computations across trust boundaries. However, ensuring complex computations with low gas fees and confidentiality remains challenging. Recent advances in Confidential Computing -- leveraging hardware-based Trusted Execution Environments (TEEs) -- and Proof-carrying Data -- employing cryptographic Zero-Knowledge Virtual Machines (zkVMs) -- hold promise for secure, privacy-preserving off-chain and layer-2 computations. On the other side, a homogeneous reliance on a single technology, such as TEEs or zkVMs, is impractical for decentralized environments with heterogeneous computational requirements. This paper introduces the Trusted Compute Unit (TCU), a unifying framework that enables composable and interoperable verifiable computations across heterogeneous technologies. Our approach allows decentralized applications (dApps) to flexibly offload complex computations to TCUs, obtaining proof of correctness. These proofs can be anchored on-chain for automated dApp interactions, while ensuring confidentiality of input data, and integrity of output data. We demonstrate how TCUs can support a prominent blockchain use case, such as federated learning. By enabling secure off-chain interactions without incurring on-chain confirmation delays or gas fees, TCUs significantly improve system performance and scalability. Experimental insights and performance evaluations confirm the feasibility and practicality of this unified approach, advancing the state of the art in verifiable off-chain services for the blockchain ecosystem.
Electric vehicles have garnered substantial attention as an environmentally sustainable transportation alternative amid escalating global concerns regarding ecological preservation and energy resource management. While the proliferation of electric vehicles necessitates the development of efficient and secure charging infrastructure, the inherent communication-intensive nature of the charging processes has raised concerns regarding potential privacy vulnerabilities. Our paper introduces a privacy protection scheme specifically designed for electric vehicle charging reservations to address this issue. The primary goal of this scheme is to protect user privacy while maintaining operational efficiency and economic viability for charging providers. Our proposed solution ensures a secure and private environment for charging reservation transactions and subsequent deviation settlements by incorporating advanced technologies, including zero-knowledge proof, a consortium blockchain, and homomorphic encryption. The scheme encrypts charging reservation information and securely transmits it via a consortium blockchain, effectively shielding the sensitive data of all participating parties. Notably, the experimental findings establish the robustness of our scheme in terms of its security and privacy protection, aligning with the stringent demands of electric vehicle charging operations.
The rise of digital healthcare has intensified concerns over data privacy, particularly in cross-institutional medical data exchanges. This study introduces a blockchain-based protocol leveraging Zero-Knowledge Proofs (ZKP), specifically zk-SNARK, to enable verifiable yet privacy-preserving health data sharing. Built on a permissioned Ethereum blockchain, the protocol ensures that medical data validity can be confirmed without disclosing sensitive content. System implementation involves Python-based zk-circuits, smart contracts in Solidity, and RESTful APIs supporting HL7 FHIR formats for interoperability. Performance evaluations show promising results: proof verification times remained under 100 ms, with average proof sizes below 2 KB, even under complex transaction scenarios. Gas consumption analysis indicates a trade-off—ZKP-enabled transactions consumed approximately 93,000 gas units, compared to 52,800 in baseline cases. Interoperability testing across 10 FHIR-based scenarios resulted in 100% parsing success and an average data integration time of 1.7 seconds. Security assessments under white-box threat models confirmed that sensitive information remains unreconstructable, preserving patient confidentiality. Compared to previous implementations using zk-STARK, this protocol offers a 30% improvement in verification efficiency and a 45% reduction in proof size. The novelty lies in combining lightweight ZKP mechanisms with an interoperability-focused design, tailored for realistic hospital infrastructures. This research delivers a scalable, standards-compliant architecture poised to advance secure digital healthcare ecosystems while complying with regulations like GDPR
Stefanos Chaliasos, Imam Al-Fath, Alastair F. Donaldson
Zero-knowledge proofs (ZKPs) have evolved from a theoretical cryptographic concept into a powerful tool for implementing privacy-preserving and verifiable applications without requiring trust assumptions. Despite significant progress in the field, implementing and using ZKPs via \emph{ZKP circuits} remains challenging, leading to numerous bugs that affect ZKP circuits in practice, and \emph{fuzzing} remains largely unexplored as a method to detect bugs in ZKP circuits. We discuss the unique challenges of applying fuzzing to ZKP circuits, examine the oracle problem and its potential solutions, and propose techniques for input generation and test harness construction. We demonstrate that fuzzing can be effective in this domain by implementing a fuzzer for \texttt{zk-regex}, a cornerstone library in modern ZKP applications. In our case study, we discovered \textit{$10$} new bugs that have been confirmed by the developers.