The rapid digital transformation being currently experienced the developing economies such as Zimbabwe has underlined the inefficiencies and vulnerabilities in security of traditional Know Your Customer (KYC) processes. These KYC processes and procedures are predominantly manual, slow and are prone to data breaches. This paper proposes a privacy preserving authentication model for KYC optimization using Zero-Knowledge Proof (ZKP) cryptography. This model addresses critical challenges which include prolonged customer onboarding times, high operational costs and data compliance risks. By means of leveraging ZKP the model enables secure identity verification without exposing sensitive data which ensures compliance with Zimbabwe Data Protection Act. A mixed-methods approach was adopted, combining qualitative and quantitative techniques to design, develop and evaluate the model. Experimental results have demonstrate significant improvements in data privacy and onboarding efficiency which has seen reduced onboarding time from 3 days to under 10 minutes. The model scalability and adaptability potential makes it suitable for diverse sectors which covers education, healthcare, e-commerce and government services therefore positioning Zimbabwe as a leader in secure digital transformation.
Blockchain technology is becoming an important tool for secure financial transactions. It supports decentralized finance (DeFi) services and new ways of auditing. This paper gives an overview of how blockchain is used in financial modeling, focusing on DeFi and auditing. We explain the basic technology behind popular blockchain systems, like public platforms such as Ethereum (with smart contracts and oracle networks), and private systems like Hyperledger Fabric. We also look at advanced methods like zero-knowledge proofs. We show how these tools help build financial models in DeFi by allowing peer-to-peer services without needing trust, and in auditing by making data more transparent and secure. We compare different blockchains in terms of speed, cost, and how well they scale. Security issues (like smart contract bugs or attacks on consensus) and practical problems (like trusting oracles and following laws) are also discussed. The review article looks at challenges in using blockchain and some of the latest solutions, such as Ethereum’s move to proof-of-stake, sharding for better scalability, and using zero-knowledge proofs for privacy. We also suggest future research topics, like connecting different blockchains, checking smart contracts with formal methods, creating better rules and laws, and training skilled workers. The goal is to help researchers and professionals understand the current situation and future of blockchain in finance and auditing.
Blockchain technology presents transformative opportunities for secure personal data sharing, particularly in healthcare, finance, and identity management. However, its widespread adoption is constrained by challenges such as limited scalability, privacy concerns, and conflicts with regulatory frameworks like the General Data Protection Regulation (GDPR). This study introduces a novel hybrid framework that integrates the InterPlanetary File System (IPFS) for off-chain storage with Zero-Knowledge Proofs (ZKPs) to enhance privacy, ensure regulatory compliance, and reduce on-chain storage demands. Employing a Design Science Research (DSR) methodology, the framework was developed and validated using Ethereum and Hyperledger Fabric, guided by insights from a systematic review of 180 studies from 2018 to 2023. Empirical evaluations revealed a 75% reduction in blockchain storage, 98% GDPR compliance, and zk-SNARK proof verification times below one second. The framework also enables GDPR-compliant erasure by removing encrypted off-chain data while preserving on-chain auditability. Despite challenges such as IPFS latency and trusted setup complexities, the solution offers a scalable and privacy-preserving architecture applicable to real-world domains, especially in privacy-critical environments like healthcare and finance by resolving blockchain’s GDPR compliance paradox.
Abstract The breaches of the blockchain wallet keys greatly harm the security of blockchain transactions. To protect the secret keys, the known solutions, such as hierarchical deterministic wallets proposed in BIP32 or stealth addresses adopted in Monero, have been extensively researched. However, most of the existing works assume the key is safe, in the sense that it cannot be stolen or damaged, which is not true in practice. Moreover, current key revocation mechanisms either rely on centralized authorities, compromising decentralization, or require economic incentives to ensure nodes remain consistantly online. In this paper, we introduce Cocoon, the first blockchain wallet scheme that supports stealth addresses and provides a wallet revocation mechanism without the need for certificates. Cocoon not only ensures the privacy of wallet secret keys but also can individually revoke compromised keys with high performance. Our contributions are three-fold: First, we present the formal model and the related security definitions. Next, we give a generic construction based on the hierarchical identity-based signature, identity-based key encapsulation mechanism and non-interactive zero-knowledge proof. We then extend the scheme to the hierarchical setting for diverse scenarios. Finally, we give the implementation, and the results show that the scheme is practical.
To cater to the needs of (Zero Knowledge) proofs for (mathematical) proofs, we describe a method to transform formal sentences in 2x2-matrices over multivariate polynomials with integer coefficients, such that usual proof-steps like modus-ponens or the substitution are easy to compute from the matrices corresponding to the terms or formulas used as arguments. By evaluating the polynomial variables in random elements of a suitably chosen finite field, the proof is replaced by a numeric sequence. Only the values corresponding to the axioms have to be computed from scratch. The values corresponding to derived formulas are computed from the values corresponding to their ancestors by applying the homomorphic properties. On such sequences, various Zero Knowledge methods can be applied.
In the evolving landscape of digital technologies, blockchain has emerged as a cornerstone for building decentralized, transparent, and tamper-proof systems. Central to the security and reliability of blockchain is the use of advanced cryptographic techniques, which ensure data integrity, user privacy, and resistance to malicious attacks. This paper provides a comprehensive analysis of cutting-edge cryptographic technologies that are shaping the future of blockchain networks. Key mechanisms discussed include zero-knowledge proofs, homomorphic encryption, ring signatures, and post-quantum cryptography. These technologies not only enhance privacy and scalability but also prepare blockchain systems to withstand future computational threats, including those posed by quantum computing. By exploring real-world implementations and potential applications, this research underscores the critical role of advanced cryptography in enabling secure, scalable, and future-ready decentralized infrastructures. The study also highlights ongoing challenges and future research directions to optimize the integration of these technologies in both public and private blockchain environments. Key Words: Blockchain, Cryptography, Zero-Knowledge Proofs, Post-Quantum Cryptography, Homomorphic Encryption, Ring Signatures, Decentralized Security, Privacy-Preserving Technologies
As large language models (LLMs) are used in sensitive fields, accurately verifying their computational provenance without disclosing their training datasets poses a significant challenge, particularly in regulated sectors such as healthcare, which have strict requirements for dataset use. Traditional approaches either incur substantial computational cost to fully verify the entire training process or leak unauthorized information to the verifier. Therefore, we introduce ZKPROV, a novel cryptographic framework allowing users to verify that the LLM's responses to their prompts are trained on datasets certified by the authorities that own them. Additionally, it ensures that the dataset's content is relevant to the users' queries without revealing sensitive information about the datasets or the model parameters. ZKPROV offers a unique balance between privacy and efficiency by binding training datasets, model parameters, and responses, while also attaching zero-knowledge proofs to the responses generated by the LLM to validate these claims. Our experimental results demonstrate sublinear scaling for generating and verifying these proofs, with end-to-end overhead under 3.3 seconds for models up to 8B parameters, presenting a practical solution for real-world applications. We also provide formal security guarantees, proving that our approach preserves dataset confidentiality while ensuring trustworthy dataset provenance.
As democratic processes increasingly transition to digital environments, safeguarding voter privacy and maintaining electoral integrity have become paramount. This study investigates the application of Zero-Knowledge Proofs (ZKPs) as a cryptographic framework for developing secure and private electronic voting systems. A comparative performance evaluation was conducted between ZKP-based voting protocols and traditional systems, focusing on key metrics such as validation time, privacy leakage index, and memory usage. Quantitative data analysis, supported by statistical methods including mean comparisons and standard deviation assessments, highlights the superiority of ZKP-based systems in minimizing information leakage while maintaining verifiability. Although ZKP protocols introduce higher memory consumption, the trade-off results in substantially enhanced voter anonymity and reduced validation latency. The findings suggest that ZKPs provide a scalable and efficient solution to the dual challenge of transparency and privacy in digital voting infrastructures. This research contributes to the growing body of work on cryptographic voting technologies and underscores the importance of balancing security with performance in the design of future e-voting systems. Keywords: Zero-Knowledge Proofs, E-voting, Cryptography, Privacy, Secure Voting Systems, Digital Democracy, Voter Anonymity, Cryptographic Protocols, Electoral Integrity, Privacy-Preserving Computation
Ensuring consistent progress toward cities’ net-zero emission goals requires understanding key dimensions of urban climate governance—particularly the motivations driving municipalities toward net zero and the critical barriers and enablers along this pathway. Current knowledge on these critical aspects is fragmented, lacking a holistic framework and empirical prioritization of key factors. We developed an integrated analytical framework and empirically distilled the most salient motivations, barriers, and enablers through a large-scale survey targeting 489 net-zero-committed municipalities—known as “Zero Carbon Cities”—across Japan. With responses from 309 municipalities, we deliver the first systematic mapping of factors perceived as most influential by Japanese local authorities. The results indicate that municipalities are primarily motivated by seizing local economic development opportunities (enhanced local energy conditions, financial gains and savings, and local industry revitalization), future-proofing communities against disasters, and enhancing the local quality of life. Key barriers and enablers were identified across four categories: municipal resources and authority (budgets, dedicated staff, and empowered climate agencies), knowledge and expertise (staff climate competence), institutional coherence (cross-departmental coordination and stakeholder involvement), and political will and leadership (the presence of climate champions and awareness within city halls and among residents). Accordingly, we discuss implications and derive recommendations toward strengthened local action in Japan and beyond.
As privacy and security concerns increase, Zero Knowledge Proof (ZKP) technology offers a promising solution for secure digital verification. ZKP addresses key privacy and security challenges across individual, business, and public sectors by enabling data protection without revealing sensitive information. The aim of this study is to analyse ZKP’s application areas by reviewing current literature and case studies, examining its strengths, limitations, and potential risks. Findings highlight the capability of ZKP to enhance privacy, security, and verification processes across various fields, including blockchain technology, identity authentication, secure data sharing, and digital voting systems. The paper provides a balanced perspective on ZKP’s benefits and challenges, including computational complexity and scalability issues. By suggesting practical use cases, this work aims to contribute to a deeper understanding of how ZKP technology can support innovation across various industries while addressing critical privacy and security needs.
Daniël Reijsbergen, Eyasu Getahun Chekole, Howard Halim, Jianying Zhou
Biometric authentication relies on physiological or behavioral traits that are inherent to a user, making them difficult to lose, forge or forget. Biometric data with a temporal component enable the following authentication protocol: recent readings of the underlying biometrics are encoded as time series and compared to a set of base readings. If the distance between the new readings and the base readings falls within an acceptable threshold, then the user is successfully authenticated. Various methods exist for comparing time series data, such as Dynamic Time Warping (DTW) and the Time Warp Edit Distance (TWED), each offering advantages and drawbacks depending on the context. Moreover, many of these techniques do not inherently preserve privacy, which is a critical consideration in biometric authentication due to the complexity of resetting biometric credentials. In this work, we propose ZK-SERIES to provide privacy and efficiency to a broad spectrum of time series-based authentication protocols. ZK-SERIES uses the same building blocks, i.e., zero-knowledge multiplication proofs and efficiently batched range proofs, to ensure consistency across all protocols. Furthermore, it is optimized for compatibility with low-capacity devices such as smartphones. To assess the effectiveness of our proposed technique, we primarily focus on two case studies for biometric authentication: shake-based and blow-based authentication. To demonstrate ZK-SERIES's practical applicability even in older and less powerful smartphones, we conduct experiments on a 5-year-old low-spec smartphone using real data for two case studies alongside scalability assessments using artificial data. Our experimental results indicate that the privacy-preserving authentication protocol can be completed within 1.3 seconds on older devices.
In a time of rising cyber threats and widespread digital communication, protecting sensitive information is crucial. This paper offers a detailed survey and analysis of current methods in secure communication, focusing on the relationship between cryptographic systems and steganographic techniques. We base our work on foundational mathematics, especially commutative algebra, and use modern technologies like Generative Adversarial Networks (GANs), zero-knowledge proofs, and quantum-resistant algorithms. We present a layered approach to information security. We discuss how combining classical and modern cryptography with improved steganographic embedding and signal processing techniques highlights the need for hybrid and adaptable models to protect communication. This study aims to be a reference point for future research and development in secure digital systems. Key Words: Cryptography : from classical to post-quantum, Steganography and Data hiding Techniques, GAN-Based Steganography in wireless sensor network , Methodology Overview.
Machine learning providers commonly distribute global models to edge devices, which subsequently personalize these models using local data. However, issues such as copyright infringements, biases, or regulatory requirements may require the verifiable removal of certain data samples across all edge devices. Ensuring that edge devices correctly execute such unlearning operations is critical to maintaining integrity. In this work, we introduce a verification framework leveraging zero-knowledge proofs, specifically zk-SNARKs, to confirm data unlearning on personalized edge-device models without compromising privacy. We have developed algorithms explicitly designed to facilitate unlearning operations that are compatible with efficient zk-SNARK proof generation, ensuring minimal computational and memory overhead suitable for constrained edge environments. Furthermore, our approach carefully preserves personalized enhancements on edge devices, maintaining model performance post-unlearning. Our results affirm the practicality and effectiveness of this verification framework, demonstrating verifiable unlearning with minimal degradation in personalization-induced performance improvements. Our methodology ensures verifiable, privacy-preserving, and effective machine unlearning across edge devices.
Open access
2 source records
cs.LG
cs.CR
Intelligent Tutoring Systems and Adaptive Learning
Cinthia Paola Pascual Cáceres, José Vicente Berná-Martínez, María Esther Almaral Martínez, Lucía Arnau Muñoz
This study introduces Fort2BCK, an advanced security framework designed to mitigate critical vulnerabilities in healthcare blockchain implementation, specifically data manipulation, unauthorised access and weaknesses in consensus protocols. Fort2BCK employs a dual verification mechanism, combining native consensus algorithm validation with the application of advanced cryptographic signatures (RSA, ECDSA and zero knowledge proofs, ZKPs), thus providing an additional layer of authentication, auditing and resistance to malicious attacks. In contrast to traditional approaches, Fort2BCK significantly reduces the risks of fraud and forgery by independently cryptographically verifying each block before it is integrated into the blockchain, strengthening security in scenarios where conventional consensus models may be vulnerable. In addition, its interoperability with multiple blockchain architectures, including proof of work (PoW), proof of stake (PoS) and delegated proof of stake (DPoS), allows it to effectively mitigate attacks such as the 51% attack in PoW and the nothing-at-stake problem in PoS, through an integrated external validation layer. To evaluate the effectiveness of Fort2BCK, experiments were conducted on a simulated hybrid blockchain network with 100 nodes and 50,000 transactions. The results revealed that Fort2BCK increases security by 35% against block rewrite attacks and decreases the rate of fraudulent transactions by 42%, compared to conventional blockchain systems, while maintaining a computational overhead of less than 8%. Additionally, Fort2BCK ensures compliance with regulations such as HIPAA and GDPR, ensuring that blockchain systems for the healthcare sector meet legal and privacy requirements. These findings demonstrate that Fort2BCK optimises the security, scalability and privacy of medical blockchains, facilitating the secure digitisation of healthcare systems and strengthening trust in clinical data management.
Federated Learning (FL) offers an attractive framework for collaboratively training AI models while preserving data privacy. However, it also introduces challenges in verifying the integrity and authenticity of model updates across diverse clients. Zero-Knowledge Proofs (ZKP) provide a promising means to address these issues by verifying computations without revealing underlying data. Yet, global verification using ZKP remains computationally expensive and does not scale well. To overcome these limitations, we propose a novel approach grounded in two key principles: (a) partial verification, targeting carefully selected subsets of data, can effectively mitigate adversarial attacks; and (b) robust data verification is essential, ensuring not only the consistency of model parameters but also the authenticity of the underlying data. We highlight the potential operation of this partial verification system, discuss novel research directions, and outline strategies for a wider integration into FL architectures.
The article investigates the problem of ensuring the veracity and traceability of production data in digital factories, where EU regulatory requirements and a high level of counterfeiting create a critical deficit of trust in source information. The objective of this study is to analyze the architectures of blockchain solutions for data verification in supply chains and to develop a phased implementation plan that considers regulatory obligations and the protection of trade secrets. The novelty lies in a combined approach: classification of DLT networks according to scalability, cost, and privacy criteria; use of Merkle trees and zero-knowledge proofs to preserve confidential data while proving authenticity; and justification of architecture choice through practical case studies (IBM Food Trust, VeChain, Airbus/Circularise, SAP). The study demonstrates that blockchain enables a reduction in batch traceability time from days to seconds, a reduction in manual operations to 67%, and an increase in data matching accuracy to 92%. However, the immutability of the ledger does not eliminate the immutable garbage problem: the veracity of records depends on sensor calibration and procedural control, which requires preliminary semantic normalization and master data management. A phased implementation enables the minimization of risks and the assessment of economic impacts. To comply with DPP, it is recommended to introduce decentralized identifiers (DID) and Verifiable Credentials, as well as the integration of zero-knowledge proofs. Thus, blockchain transitions from a trial technology to a necessary component of the practical infrastructure for sustainable production chains. This article will help managers and experts in digitalization and supply chain management.
This paper explores how zero-knowledge proofs can enhance Bitcoin's functionality and privacy. First, we consider Proof-of-Reserve schemes: by using zk-STARKs, a custodian can prove its Bitcoin holdings are more than a predefined threshold X, without revealing addresses or actual balances. We outline a STARK-based protocol for Bitcoin UTXOs and discuss its efficiency. Second, we examine ZK Light Clients, where a mobile or lightweight device verifies Bitcoin's proof-of-work chain using succinct proofs. We propose a protocol for generating and verifying a STARK-based proof of a chain of block headers, enabling trust-minimized client operation. Third, we explore Privacy-Preserving Rollups via BitVM: leveraging BitVM, we design a conceptual rollup that keeps transaction data confidential using zero-knowledge proofs. In each case, we analyze security, compare with existing approaches, and discuss implementation considerations. Our contributions include the design of concrete protocols adapted to Bitcoin's UTXO model and an assessment of their practicality. The results suggest that while ZK proofs can bring powerful features (e.g., on-chain reserve audits, trustless light clients, and private layer-2 execution) to Bitcoin, each application requires careful trade-offs in efficiency and trust assumptions.
The rapid evolution of phishing attacks targeting email, chat, and social media platforms poses a significant threat to digital security, with a reported 667% surge in spear-phishing during the 2020 COVID-19 crisis [1]. Current AI-based detection systems face challenges in dataset diversity, adversarial robustness, computational scalability, model interpretability, and privacy preservation, limiting their efficacy in real-time, multi-platform environments. This paper introduces PhishGuard, an innovative framework for real-time phishing detection, designed to overcome these limitations. PhishGuard integrates lightweight transformer models (e.g., distilled BERT), hybrid detection techniques combining natural language processing (NLP), propagation analysis, and user behavior analysis, and explainable AI (XAI) methods like SHAP and LIME for transparent decision-making. Privacy-preserving techniques, including federated learning and local differential privacy, ensure secure processing of sensitive user data. Evaluated on diverse datasets such as PhiKitA, Enron, and a custom social media corpus, PhishGuard achieves up to 97.5% accuracy, 94% F1-score, and inference times below 5 ms, demonstrating scalability for resource-constrained devices. The framework also incorporates zero-knowledge proofs for verifiable inference, addressing trust and integrity concerns. By tackling cross-domain generalization, adversarial robustness, and real-time performance, PhishGuard offers a scalable, user centric solution for secure digital communications, with applications in finance, healthcare, and social media platforms. Future enhancements include multilingual support and image based phishing detection, paving the way for a comprehensive defense against evolving cyber threats.
Jessica Man, Sadiq Jaffer, Patrick Ferris, Martin Kleppmann · 5 authors
Information and Communication Technologies (ICT) have a significant climate impact, and data centres account for a large proportion of the carbon emissions from ICT. To achieve sustainability goals, it is important that all parties involved in ICT supply chains can track and share accurate carbon emissions data with their customers, investors, and the authorities. However, businesses have strong incentives to make their numbers look good, whilst less so to publish their accounting methods along with all the input data, due to the risk of revealing sensitive information. It would be uneconomical to use a trusted third party to verify the data for every report for each party in the chain. As a result, carbon emissions reporting in supply chains currently relies on unverified data. This paper proposes a methodology that applies cryptography and zero-knowledge proofs for carbon emissions claims that can be subsequently verified without the knowledge of the private input data. The proposed system is based on a zero-knowledge Succinct Non-interactive ARguments of Knowledge (zk-SNARK) protocol, which enables verifiable emissions reporting mechanisms across a chain of energy suppliers, cloud data centres, cloud services providers, and customers, without any company needing to disclose commercially sensitive information. This allows customers of cloud services to accurately account for the emissions generated by their activities, improving data quality for their own regulatory reporting. Cloud services providers would also be held accountable for producing accurate carbon emissions data.
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.
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.