Blockchain Papers

Zenebe Melesew Yetneberk, Tong-Xing Zheng, Xinji Wang, Haiyang Cao · 7 authors

No abstract is available for this record.

S. Arunadevi, P. Valarmathie

The secure management of Electronic Health Records (EHRs) in a cloud environment poses many challenges, and guaranteeing the scalability of a secure solution to manage the huge amounts of data and its privacy remains an open problem. Although the existing encryption methods offer strong security, the widespread adoption of asymmetric-key protocols is limited because of the lack of computational efficiency compared with practical applications. That is, the efficiency of computational time required for an encryption or decryption step, and the privacy-preserving verification of the computation result, are not balanced due to the volume of the data. In order to overcome the drawbacks of existing encryption methods, we propose a novel approach to integrating Zero-Knowledge Proofs (ZKPs) with a Multi-Layer Merkle Tree (MLMT) to achieve a scalable, privacy-preserving, guarantee-of-integrity and publicly-verifiable solution for managing the EHR while ensuring patients’ privacy. It proposes the utilization of MLMT to build a hierarchical data verification structure for massive data, significantly improving the computational efficiency. It also employs ZKP to enable verifier to verify the validity of data without revealing any record information, which is vital for the data management in the healthcare sector. The authors compared the proposed model with existing approaches which adopt AES-256 encryption and typical Merkle Tree-based solutions and demonstrate its superior scalability and privacy-preserving ability while ensuring controllable computational overhead. The results demonstrate that ML MMT ZKP provides the best balance between privacy, integrity and scalability reaching lower overheads and shorter verification times than other traditional approaches. This work constitutes a step forward in the development of cryptographic solutions for EHRs and provides a framework for real-time verifiable information in the healthcare domain.

SATI, Vidisha

The cryptographic protocol developments are transforming digital trust is the capacity to verify without revealing any underlying information. Traditional authentication and authorization systems are usually prone to leakage of sensitive data, resulting in compromise of privacy and low scalability in distributed systems. The root of these problems is eliminated through the so-called zero-knowledge techniques that allow demonstrating to one party ownership of some information without exposing it. This paper explores the origin and development of zero-knowledge protocols in light of its efficiency, trustless design, and privacy focus to illustrate why the application is worth the hype. Particular attention is paid to such structures as zk-SNARKs, zk-STARKs, and bulletproofs, as well as their application to constructing transparent, scalable systems. Blockchain aptitudes used anywhere in confidentiality of transactions, decentralized identity systems allow a self-sovereign identity without exaggerating personal information, and the healthcare and finance industries enjoy the ability to share information securely without any effect on compliance aspects. The next discussion points are implementations, the scalability issue, cryptographic assumptions, and integration issues. This survey outlines evaluations of deployments from 2021 to 2025 to determine the following top benefits, barriers, and trends in building systems that safeguard privacy without compromising their performance or trust to the client. Future requirement conclusions provide some insights about future requirements in terms of efficient construction of proofs, standardizations, and ease of usability to expand the adoption of infrastructures built on zero-knowledge into a constantly more integrated digital world.

Santiago Germino, Martín N. Menéndez, Ariel Lutenberg

Essential infrastructure and services depend on critical systems. To ensure that critical systems function properly, regular testing and monitoring are necessary. Establishing direct, dedicated data connections for remote testing can be expensive, while using public cellular, satellite, or fiber Internet connections can introduce privacy and security risks. Securing the medium often requires placing trust in third parties. The novel proposal introduced in this work suggests using zero-knowledge proofs, a modern cryptographic technique, to conduct secure remote testing and monitoring of critical systems over affordable public networks, which can include email or instant messaging apps. This approach guarantees both the integrity and confidentiality of the transmitted data, as well as the integrity of the processes involved in preparing the data for transmission. We will present this approach and demonstrate its implementation through a real-world use case: the remote testing of an electronic railway interlocking system.

Peifang Ni, Jing Xu

No abstract is available for this record.

П.Д. Павел

Существующие типовые модели аутентификации с использованием цифровых удостоверений носят абстрактный характер. Для конкретизации модели аутентификации с использованием цифровых удостоверений предлагаются: алгоритм эмиссии цифровых удостоверений; алгоритм аутентификации на основе доказательства с нулевым разглашением. Производится количественная оценка раскрытых данных удостоверений в результате: предложенного алгоритма аутентификации на основе доказательства с нулевым разглашением; аутентификации с полным раскрытием атрибутов; аутентификации с частичным раскрытием атрибутов. Полученные результаты оценок анализируются и делаются соответствующие выводы. Existing standard authentication models using digital credentials tend to be abstract. To refine the authentication model using digital credentials, the following are proposed: a digital credential issuance algorithm and an authentication algorithm based on zero-knowledge proof. A quantitative assessment is conducted on the amount of disclosed credential data resulting from the proposed zero-knowledge proof-based authentication algorithm, authentication with full attribute disclosure, and authentication with partial attribute disclosure. The assessment results are analyzed, and relevant conclusions are drawn.

Daniel Commey, Benjamin Appiah, Griffith Selorm Klogo, Garth V. Crosby

Federated Learning (FL) enables collaborative model training on decentralized data without exposing raw data. However, the evaluation phase in FL may leak sensitive information through shared performance metrics. In this paper, we propose a novel protocol that incorporates Zero-Knowledge Proofs (ZKPs) to enable privacy-preserving and verifiable evaluation for FL. Instead of revealing raw loss values, clients generate a succinct proof asserting that their local loss is below a predefined threshold. Our approach is implemented without reliance on external APIs, using self-contained modules for federated learning simulation, ZKP circuit design, and experimental evaluation on both the MNIST and Human Activity Recognition (HAR) datasets. We focus on a threshold-based proof for a simple Convolutional Neural Network (CNN) model (for MNIST) and a multi-layer perceptron (MLP) model (for HAR), and evaluate the approach in terms of computational overhead, communication cost, and verifiability.

Rui Ding, Shaoyi Xu, Liyan Wu

The globalization of digital infrastructures necessitates secure cross-border data transfers, yet existing governance frameworks struggle to reconcile regulatory transparency requirements with enterprise needs for confidentiality. Traditional approaches based on trusted execution environments or blockchain technologies face critical limitations, including prohibitive operational costs and technical inflexibility across cryptographic standards. This paper introduces a novel cryptographic framework that systematically addresses these challenges through three core innovations. First, we establish a lifecycle model integrating transmission, attestation, and verification phases with deterministic cryptographic constraints, ensuring continuous integrity monitoring across distributed systems. Second, our architecture implements non-intrusive compliance validation through zero-knowledge proofs and privacy-preserving verification protocols, eliminating raw data exposure while meeting diverse regulatory mandates. Third, the framework achieves interoperability across conflicting digital certification standards through adaptive policy mappings. Experimental evaluations demonstrate the solution's superiority over conventional approaches, showing significant improvements in verification efficiency, reduced resource consumption, and robust defense against tampering attacks. The proposed model supports multi-jurisdictional legal requirements through auditable cryptographic proofs and timestamped evidence chains, offering enterprises a practical pathway for compliant cross-border operations. By embedding regulatory logic into technical workflows, our approach advances secure global data ecosystems that balance sovereignty preservation with digital economy demands.

Artem Chystiakov, Mariia Zhvanko

Transparency is one of the key benefits of public blockchains. However, the public visibility of transactions potentially compromises users' privacy. The fundamental challenge is to balance the intrinsic benefits of blockchain openness with the vital need for individual confidentiality. The proposal suggests creating a confidential version of wrapped Ethereum (cWETH) fully within the application layer. The solution combines the Elliptic Curve (EC) Twisted ElGamal-based commitment scheme to preserve confidentiality and the EC Diffie-Hellman (DH) protocol to introduce accessibility limited by the commitment scheme. To enforce the correct generation of commitments, encryption, and decryption, zk-SNARKs are utilized.

Sofia Sakka, Nikolaos Pavlidis, Vasiliki Liagkou, Ioannis Panges · 7 authors

The growing influence of technology in the healthcare industry has led to the creation of innovative applications that improve convenience, accessibility, and diagnostic accuracy. However, health applications face significant challenges concerning user privacy and data security, as they handle extremely sensitive personal and medical information. Privacy-Enhancing Technologies (PETs), such as Privacy-Attribute-based Credentials, Differential Privacy, and Federated Learning, have emerged as crucial tools to tackle these challenges. Despite their potential, PETs are not widely utilized due to technical and implementation obstacles. This research introduces a comprehensive framework for protecting health applications from privacy and security threats, with a specific emphasis on gamified mental health apps designed to manage Attention Deficit Hyperactivity Disorder (ADHD) in children. Acknowledging the heightened sensitivity of mental health data, especially in applications for children, our framework prioritizes user-centered design and strong privacy measures. We suggest an identity management system based on blockchain technology to ensure secure and transparent credential management and incorporate Federated Learning to enable privacy-preserving AI-driven predictions. These advancements ensure compliance with data protection regulations, like GDPR, while meeting the needs of various stakeholders, including children, parents, educators, and healthcare professionals.

Serhan W. Bahar

The emergence of quantum computing presents profound challenges to existing cryptographic infrastructures, whilst the development of central bank digital currencies (CBDCs) has raised concerns regarding privacy preservation and excessive centralisation in digital payment systems. This paper proposes the Quantum-Resilient Privacy Ledger (QRPL) as an innovative token-based digital currency architecture that incorporates National Institute of Standards and Technology (NIST)-standardised post-quantum cryptography (PQC) with hash-based zero-knowledge proofs to ensure user sovereignty, scalability, and transaction confidentiality. Key contributions include adaptations of ephemeral proof chains for unlinkable transactions, a privacy-weighted Proof-of-Stake (PoS) consensus to promote equitable participation, and a novel zero-knowledge proof-based mechanism for privacy-preserving selective disclosure. QRPL aims to address critical shortcomings in prevailing CBDC designs, including risks of pervasive surveillance, with a 10-20 second block time to balance security and throughput in future monetary systems. While conceptual, empirical prototypes are planned. Future work includes prototype development to validate these models empirically.

Shuai Wang, Youliang Tian, Jinbo Xiong, Jianfeng Ma · 5 authors

Federated Learning (FL) enables resource-constrained nodes in edge intelligence to train a global model using local data under the coordination of a server without the risk of privacy disclosure. Secure aggregation employs security primitives to encrypt and compute local gradients, enhancing the security attributes of vanilla FL. However, server-driven FL faces communication bottlenecks and high trust risks when coordinating large-scale distributed devices, and the existing secure aggregation with input validation schemes can only verify input vectors of lengths that are powers of 2. In this work, we propose VerifyDFL, a distributed secure aggregation protocol with input validation, which enables clients to locally validate the gradients of others within the decentralized federated learning (DFL) paradigm. Specifically, we propose a distributed proof approach based on Springproofs that supports arbitrary-length input validation. Clients locally verify the L∞ and L2 norms of others’ inputs with a zero-knowledge manner. Furthermore, we employ k-regular graphs to enhance the communication topology of DFL, which guarantees that each client can securely aggregate gradients locally even when corrupted or dropped clients participate in federated training. The security analysis and proofs ensure that VerifyDFL meets the privacy protection requirements of DFL. We conduct real benchmark experiments to show that VerifyDFL optimizes the computational cost by approximately 20% over the state-of-the-art input validation protocols. Additionally, VerifyDFL enforces L∞ and L2 norm correctness verification on encrypted model gradients in edge intelligence.

Yuxiao Wu, Yutaka Matsubara, Shoji Kasahara

Blockchain and smart contracts are widely used in IoT access control to create decentralized, trustworthy environments for secure access and record management. However, their application introduces a dual challenge: The transparency of blockchain and the use of addresses as identifiers can expose account privacy. To tackle this issue, this paper proposes a blockchain-based IoT access control system that enhances account anonymity and preserves privacy, particularly regarding user behavior, habits, and access records through the use of zero-knowledge proofs. The system incorporates an access control mechanism that combines access control lists with capability-based access control, enabling ownership verification of access rights without disclosing identity information. To evaluate the system’s feasibility, we conduct experiments in a smart building scenario, including both qualitative comparisons with existing methods and quantitative analyses of performance in terms of time, space, and gas consumption. The results indicate that our scheme achieves the best time efficiency in the proof generation and authorization phases, completing them in just 7 and 10 s, respectively—representing half the time required by the second-best approach. These findings underscore the system’s superior cost efficiency and enhanced security compared to existing solutions.

Chen Lin, Yanli Ren, Zheng Guo, Yangrui Mo

Decentralized anonymous payment (DAP) solves the privacy leakage problem in decentralized payment. However, some criminals may use DAP to carry out illegal activities, since DAP supports unconditional privacy protection and illegal transactions are difficult to be identified and tracked. Some studies have introduced regulatory mechanisms in DAP to track illegal transactions, but there are still problems such as privacy disclosure and low regulatory efficiency. In this paper, we propose an efficient two-level supervision scheme ETLS, which aggregates anonymous transactions based on Walsh commitment. The first-level regulator only needs to process the aggregation results to screen out suspicious users, and the second-level regulator discloses the public keys of suspicious users. During the supervision process, only the public keys of suspicious users will be identified, and the privacy of compliant transactions will be kept confidential. Compared to previous works, the ETLS scheme greatly improves regulatory efficiency while ensuring the privacy of transaction address and payment amount. The security properties of the ETLS scheme are defined and proved based on the security of DAP system, the security of Walsh commitment and zero-knowledge proof, and its performance is tested based on the Zcash system. The findings demonstrate that the ETLS scheme can effectively strike a compromise between privacy protection and regulatory requirements while maintaining low computational and communication overheads.

Robert Canady, Chandreyee Bhowmick, Xenofon Koutsoukos

Federated learning has become increasingly popular for its ability to process large, distributed datasets and speed up learning while protecting data privacy. However, it typically relies on a central server for coordination, which can be a bottleneck and a single point of failure. To address these limitations, we developed a novel distributed learning architecture that eliminates the need for a central server. The architecture utilizes the hashgraph consensus algorithm (HCA), a distributed ledger technology, which enables the computing nodes to train machine learning models using local data and aggregation with models received from their neighbors. Our work demonstrates that distributed learning using hashgraph consensus can be performed efficiently and is a valid alternative to traditional federated learning. To strengthen this claim, we analyze resilient federated learning in a decentralized setting. Our analysis includes scenarios with denial-of-service and model poisoning attacks. We introduce trimmed soft-medoid (TSM), a resilient aggregation method that has proven resilience to model poisoning attacks. It can be performed at every node using the information available from the hashgraph. An extensive evaluation is conducted using two multimodal machine learning tasks, human emotion recognition and activity recognition. The results confirm that decentralized learning using hashgraph consensus maintains performance parity with traditional federated learning using a central server. This is shown in both normal and adversarial scenarios. We also evaluate the latency and memory overhead of the proposed approach. These are reported to be under an acceptable range, latency of 1s and memory overhead of 8.8-13 GB, for decentralized machine learning.

Oleksandr Kurbatov, Kyrylo Baibula, Yaroslava Chopa, Sergey Kozlov · 14 authors

This paper presents Wrapless -- a lending protocol that enables the collateralization of bitcoins without requiring a trusted wrapping mechanism. The protocol facilitates a "loan channel" on the Bitcoin blockchain, allowing bitcoins to be locked as collateral for loans issued on any blockchain that supports Turing-complete smart contracts. The protocol is designed in a way that makes it economically irrational for each involved party to manipulate the loan rules. There is still a significant research area to bring the protocol closer to traditional AMM financial instruments.

Agathe Beaugrand

Arguments à divulgation nulle de connaissance efficaces et succincts dans le cadre du chiffrement CL et applications Le schéma de chiffrement CL est un système de chiffrement à clé publique linéairement homomorphe, proposé en 2015 par Castagnos et Laguillaumie. Il repose sur l’utilisation de groupes de classes de corps quadratiques imaginaires. Ces groupes finis ont la particularité d’être considérés d’ordre inconnu, c’est-à-dire que l’ordre d’un tel groupe est difficile à déterminer de manière algorithmique. Cet ordre inconnu est un atout précieux pour les applications cryptographiques, et est central dans la construction du chiffrement CL. Cependant, il est aussi à l’origine d’importantes difficultés techniques liées à la manipulation de chiffrés CL. Dans ce contexte, la construction d’arguments, et à fortiori d’arguments de connaissance, à divulgation nulle de connaissance est particulièrement exigeante, et constitue un défi majeur à relever. En effet, les techniques classiques permettant d’améliorer l’efficacité des preuves dans le cas d’un groupe d’ordre premier, et en particulier celles liées à la robustesse, s’adaptent mal au cas de l’ordre inconnu. Les arguments de connaissance existants sont donc souvent peu efficaces, avec des coûts de communication et de calcul élevés. Dans cette thèse, nous concevons de nouveaux protocoles à divulgation nulle de connaissance spécifiquement adaptés au cadre du chiffrement CL, afin d’obtenir des preuves plus courtes et efficaces que les protocoles existants. Nos protocoles reposent sur deux outils principaux : le premier est l’hypothèse C-rough, introduite par Braun, Damgard et Orlandi en 2023. Cette hypothèse algorithmique spécifique au cadre de CL stipule qu’il est difficile de décider si l’ordre d’un groupe de classes engendré par l’algorithme d’initialisation de CL possède des facteurs premiers plus petit qu’un seuil C. Le second est un concept novateur appelé extractabilité partielle, qui correspond à une notion affaiblie de robustesse de la connaissance. Cette notion est particulièrement adaptée au cadre de CL, car elle permet de traiter séparément les textes clairs et les aléas apparaissant dans les chiffrés CL. En particulier, elle permet d’exploiter les techniques du cas de l’ordre premier pour obtenir de l’information sur les textes clairs – définis modulo un nombre premier connu – même si les aléas sont définis modulo un entier composé et, surtout, inconnu. Grâce à ces deux outils, nous construisons des protocoles à divulgation nulle de connaissance permettant de prouver, d’une part, des énoncés classiques, comme le fait qu’un chiffré CL est bien formé, et d’autre part, des énoncés plus spécifiques, tels que le mélange aléatoire de chiffrés. Les preuves à divulgation nulle de connaissance sont essentielles à la sécurité des protocoles de calcul multipartite, en particulier face à des adversaires malveillants, car elles permettent de garantir que les participants se comportent conformément au protocole. Ainsi, disposer de preuves efficaces pour le chiffrement CL représente une étape fondamentale dans la construction de protocoles de calcul distribué pratiques et sûrs utilisant CL. En application de nos techniques, nous présentons un protocole, sûr en présence d’un adversaire malveillant, qui réalise la fonctionnalité “PSI-sum” – une variante de l’intersection privée d’ensembles. Cet exemple pratique met en évidence l’intérêt du chiffrement CL comme bloc de base pour réaliser des fonctionnalités avancées de calcul multipartite.

Carlo Segat, Sandro Rodriguez Garzon, Axel Küpper

Self-Sovereign Identity (SSI) is a paradigm for digital identity management that offers privacy and flexibility advantages. A key technology in SSI is Decentralized Identifiers (DIDs) and their associated metadata, DID Documents (DDOs). DDOs contain crucial verification material such as the public keys of the entity identified by the DID (i.e., the DID subject) and are often anchored on a distributed ledger to ensure security and availability. Long-lived DIDs must support updates (e.g., key rotation). Ideally, only the DID subject should authorize DDO updates. However, in practice, update capabilities may be shared or delegated. While the DID specification acknowledges such scenarios, it does not define how updates should be authorized when multiple entities jointly control a DID (i.e., group control). This article examines the implementation of an on-chain, trustless mechanism enabling DID controllers under group control to program their governance rules. The main research question is the following: Can a technical mechanism be developed to orchestrate on-chain group control of a DDO in a ledger-agnostic and adaptable manner?

Annupriya, Hatesh Shyan, Amanpreet Kaur

Existing traditional lottery systems are often prone to issues of fraud, transparency and centralized authority, which compromise the degree of fairness and security involved in the lottery process. The current work offers a blockchainbased traditional lottery system that is based on smart contracts and offers a decentralized, tamper-proof and transparent environment. Using cryptographic hashing and distributed ledger technology, the proposed system eliminates intermediaries, reduces operating costs by as much as 35 % and increases user trust by 87 %. The automated functioning of smart contracts means that the entire lottery process from the issuance of tickets to how winners are chosen and paid is immutable and verifiable. The proof-of-concept implementation using Ethereum and solidity demonstrated improvements in efficiency, such as shortening the payout time from$24-48$hours to less than 10 seconds and exhibiting 100 % resistance to known smart contract vulnerabilities. The empirical evidence highlights the potential of blockchain technology to transform the way digital lotteries are conducted by providing the benefits of security, fairness and transparency.

F. Leo John

No abstract is available for this record.

Vittoria Bonanzinga, Mariantonia Cotronei, Gioia Failla, Sofia Giuffré · 5 authors

The increasing use of localization devices for location-based services has led to an explosion in user location data. This raises significant privacy concerns that often conflict with the need for identification and accountability in critical scenarios like criminal investigations or public health emergencies. Research is facing the challenge of balancing privacy with data utility, guaranteeing trust in verification. This paper proposes a novel blockchain-based solution to reconcile the conflicting requirements of user privacy and accountability in localization. Our scheme leverages the transparency and immutability of blockchain to record verifiable location proofs. To ensure user privacy against routine disclosure, the solution integrates elliptic curve cryptography and Zero-Knowledge Proofs, allowing a verifier to confirm a user's presence without revealing sensitive information. Our solution also prevents the verifier from disclosing proof of a user's past presence to third parties, further enhancing privacy. Moreover, the proposed system provides a mechanism for accountability, allowing a designated authority to override privacy safeguards and access location data when legally mandated for public interest reasons, thereby reconciling privacy and identification needs.

Dilli Ganesh, T J Nandhini, Amer Ibrahim, Ahmed A. Elngar · 5 authors

With the current prevalence of digitization of health care records comes the issues of data privacy, security, and interoperability typical in traditional health information systems. This paper proposes a Blockchain-Powered Secure Health Data Exchange that utilizes smart contracts, cryptographic algorithms (AES-256, ECDSA), and a decentralized ledger to improve patient privacy and interoperability. This paper presents a novel blockchain-based monitoring mechanism tailored for EHRs: RUDDER—real-time, universal, decentralized, distributed, and enciphered data regulation for EHRs. Using role-based access control (RBAC) and zero-knowledge proofs (ZKP), the architecture prevents unauthorized access in our patient-centric model. This approach enabled the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, which offers high transaction throughput and latency. In addition, we create an interoperability layer that is FHIR compliant and allows for continued data exchange between the hospitals, research institutions, and the insurer. Experimental results show that significant gains have been achieved with a 500% increase in scalability, 99.6% lower operational costs, and 90% lower energy consumption compared to their conventional counterparts. The new framework that was proposed is a scalable, secure, and cost-effective solution for next-generation healthcare data management. The future work will cover AI-based anomaly detection and quantum-resistant cryptography that can improve security and efficiency.

Tholfiqar Z. Ismail, Mehdi Ebady Manaa, Durbek Sayfullaev, Muhidinov Ayubbek Nuritdinovich · 6 authors

Electronic voting systems are increasingly being explored to enhance accessibility, efficiency, and speed in modern electoral processes. However, ensuring vote integrity, privacy, and auditability remains a major challenge, especially in remote and online voting environments. Traditional electronic voting methods often face issues such as data tampering, lack of end-to-end verifiability, and potential privacy breaches, which undermine public trust and electoral transparency. To address these issues, this paper proposes the ZK-VOTE (Zero-Knowledge Verified Online Tamper-resistant Election) framework, which integrates zk-SNARKs with a permissioned blockchain protocol. In this system, voters generate zero-knowledge proofs to verify their eligibility and the validity of their votes without disclosing sensitive information. Each vote is immutably recorded on a permissioned blockchain, ensuring transparency while maintaining voter anonymity. The use of consensus algorithms prevents unauthorized alterations to voting records, and smart contracts automatically enforce vote submission rules. The ZK-VOTE framework is particularly suited for national-scale elections, enabling secure remote voting for diaspora populations while ensuring system-wide auditability. Election authorities and third-party auditors can independently verify election results without accessing private voter data. Experimental evaluation and theoretical analysis demonstrate that the proposed method achieves high levels of privacy, resistance to tampering, and verifiability. Results confirm that ZK-VOTE enhances voter trust and electoral transparency while remaining computationally efficient. The framework represents a significant advancement toward secure, scalable, and trustworthy electronic voting systems.

Ashu Nayak, S. Sivasubramanian, Anil Sharma, Hasan M. Madi · 7 authors

The rising need for safe and privacy-preserving digital identity verification has exposed the limits of previous methods, which typically involve personal information. These technologies risk users' privacy owing to data leaks and user-centric management issues. Due to these problems, this article presents ZK-VerifyChain, a blockchain-based Zero-Knowledge-based Verification system. The proposed ZK-VerifyChain uses permissioned blockchain smart contracts and non-interactive zero-knowledge proofs to verify identities securely. With this design, users can identify themselves without giving personal information. The suggested technique was tested in a virtual environment for computational overhead, scalability, and verification time. Testing has demonstrated that the ZK-Verify Chain is effective, fast, and secure against manipulation and identity theft. The blockchain layer provides immutability, transparency, and decentralized trust management. The suggested ZK-VerifyChain moves us closer to user-controlled, secure digital identity systems by providing a scalable, privacy-centric digital identity verification solution. The experimental results show an average verification time of 12.4 ms compared to other methods.