Aiming at the current problems of cross-institutional mutual recognition, poor traceability and lack of trust in general education credit management, this study proposes a blockchain technology-based credit banking system construction scheme. The decentralised storage and sharing of credit data is achieved through the design of distributed ledger architecture, and smart contract technology is used to automate the execution of credit authentication, accumulation and conversion rules. Construct a standardised model of credit metadata, define the blockchain storage format of course code, credit hours, grades and other data fields, and apply the improved PBFT consensus mechanism to improve the transaction processing efficiency while ensuring data consistency. Zero-knowledge proof technology is used to realise privacy protection and ensure the security of students' sensitive information when it flows across institutions. Experimental results show that the system improves the efficiency of credit verification by 68% compared with the traditional centralised system.
Protecting user rights and enabling cross-border interoperability require the existence of private and secure management of personal identity data in digital environments. To address this need in the context of electronic health cards for digital identification and healthcare services, this paper proposes a novel architecture that integrates self-sovereign identity (SSI), verifiable credentials (VC), and zero- knowledge proofs (ZKP). With SSI, users maintain full control over their data, storing and sharing identity credentials through digital wallets using decentralized identifiers (DIDs). VC enables trusted organizations to issue authenticated, tamper-proof digital certificates, while ZKPs allow individuals to prove identity-related claims without disclosing unnecessary personal details. The combination of these technologies enables a framework that ensures a tradeoff between user privacy and security processes. The findings of the tests achieved indicate that our framework, compared to conventional models, optimizes the interoperability and privacy of e-health card systems. Additionally, the suggested solution supports recent data protection regulations and demonstrates strong potential for global adoption.
Information Communication Technologies, Deniz Salucu, Tunga Sayıcı, Information Communication Technologies
The rapid rise of adoption of digital identity presents a transformative opportunity to eliminate resource-intensive physical identity systems, no longer requiring printed cards, plastic credentials, or in-person office visits, thereby substantially reducing carbon footprints. This paper introduces a comprehensive Self-Sovereign Identity (SSI) model built upon Hyperledger Indy, enabling end users to securely store and manage their identities directly on personal devices. We explore two different enrollment methods: QR code-based digital credential issuance and NFC-powered chip-based identity verification, further enhanced through zero-knowledge proofs and verifiable credential protocols. The ecological advantages are emphasized through the eradication of physical identity artifacts and associated administrative processes, advancing the field of green digital technologies in support of ecological preservation and sustainable identity infrastructures.
Increasing demand in innovative healthcare systems, as well as information management, enforces institutions and private consortiums to enroll in decentralized solutions that preserve patient’s sensitive information, and give capability of revoking and grating access to specific entities that request patient data. With blockchain emerging technology these solutions can be achieved allowing a more user-centric control of their own data. Furthermore, the need to conceal information and disabling data mining algorithm from agglomerating patient’s data and correlate them to their corresponding healthcare providers. This is crucial to maintain several privacy issues introduced by third parties accessing patient data without the patient’s explicit consent and applying those algorithms to perform clinical studies. This paper proposes an architectural approach at solving the problem of privacy preserving data sharing agreements between patients and healthcare providers, using blockchain, smart contracts and zero-knowledge proofs.
Zaki Rangwala, Stefan Neskovic, Amirhossein Kompanizare
This study examines how well SSL/TLS and blockchain work to secure online transactions, especially purchase orders. Reviewing literature from 2013 to 2024 shows each method's key themes, benefits, and weaknesses. SSL/TLS is known for its strong encryption and solid framework but often has issues like centralization and threats from different attacks, leading to transaction delays and bottlenecks. On the other hand, blockchain technology uses decentralized protocols and features such as zero-knowledge proofs, promising better scalability and security, allowing for smooth and safe transactions without traditional middlemen. This comparison clarifies how effective each method is. It highlights the rise of blockchain as a viable option to the limits of SSL/TLS, deserving more study in e-commerce security.
Seid Mehammed, Girma Bewuketu, Demeke Getaneh, Md Nasre Alam · 6 authors
We present a permissioned blockchain–audited federated learning (FL) framework that strengthens data provenance and model‐update integrity. Our contribution is primarily engineering and architectural: a modular two‐channel design (provenance vs. update‐audit), lightweight on‐chain validation with off‐chain analytics, and a practical mapping to the 1 + 5 architectural views. In a TensorFlow Federated + Hyperledger Fabric prototype with 10 clients, we observe ≈18% faster anomaly detection under attack and a + 0.4 pp accuracy delta versus a baseline FL setup, with ~6% communication and ~8% energy overhead. We also provide a proof‐of‐concept zero‐knowledge succinct noninteractive argument of knowledge (zk‐SNARK) flow to validate per‐client summary properties off‐chain while anchoring results on‐chain. These contributions collectively advance the practical deployment of secure, auditable FL systems.
The number theoretic transform (NTT) provides a practical and efficient technique to perform multiplication of very large degree polynomials typically found in fully homomorphic encryption (FHE), lattice-based cryptography, and non-interactive succinct zero-knowledge proof systems such as zk-SNARK. In this paper, we focus on this aspect and present two robust algorithms for efficient NTT using readily available GPU cards as hardware accelerators. These algorithms are based on the radix-2 Cooley-Tukey (CT) and 4-Step techniques, which are rooted in classical FFT research. To this end, our algorithms leverage novel strategy to optimize memory access patterns adaptive to input size, which often is very large. Our approach: i) reduces and optimizes the number of accesses required for global memory for thread synchronization on the GPU device, and ii) systematically improves and enhances the use of spatial locality. We achieve this effect by carefully controlling parameters such as the number of kernels, thread block size and shape, and thread layout, which directly impact overall NTT performance. The proposed optimizations enable our NTT implementation to handle very large polynomial sizes up to 228, which are usually a limiting factor in existing approaches, and achieve remarkable performance. To the best of our knowledge, our proposed technique is unique and provides a recipe for selecting suitable configurable parameter combinations to achieve top performance for a given polynomial degree. Furthermore, we perform thorough experiments and empirically assess the performance of our proposed algorithms on three mainstream commercial GPU cards by NVIDIA. Finally, we demonstrate that our algorithms compare favorably and outperform an existing commercial-grade open-source implementation in this arena.
<title>Abstract</title> Proposes Reg-Twin—a digitalization of the end-to-end process from transaction → alert → case → SAR/SEC reporting. It incorporates a built-in policy rule engine, reconciliation and drift monitoring, and strategy A/B sandboxing. Through hash-chain data inheritance + selective zero-knowledge proofs (ZK), it demonstrates key compliance points to regulators without data leakage. In simulations and replays across 3,500+ funds and 70+ institutions: - Consistency defects reduced by 41% - Cross-report discrepancies decreased by 36% - Closing cycles shortened by 22% - Estimated alert volume/personnel efficiency/SLA error for strategy changes ≤ ±5% ZK proofs minimize sensitive field disclosure while enabling auditable verification. Reg-Twin demonstrates a technical pathway where enhanced transparency coexists with reduced compliance costs.
Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive, in which a prover convinces a verifier that a given statement is true without leaking any additional information.However, existing zkSNARKs suffer from high computation overhead in the proof generation.This limits the applications of zkSNARKs, such as private payments, private smart contracts, and anonymous credentials.Private delegation has become a prominent way to accelerate proof generation.In this work, we propose Siniel, an efficient private delegation framework for zkSNARKs constructed from polynomial interactive oracle proof (PIOP) and polynomial commitment scheme (PCS).Our protocol allows a computationally limited prover (a.k.a.delegator) to delegate its expensive prover computation to several workers without leaking any information about the private witness.Most importantly, compared with the recent work EOS (USENIX'23), the state-of-the-art zkSNARK prover delegation framework, a prover in Siniel needs not to engage in the MPC protocol after sending its shares of private witness.This means that a Siniel prover can outsource the entire computation to the workers.We compare Siniel with EOS and show significant performance advantages of the former.The experimental results show that, under low bandwidth conditions (10MBps), Siniel saves about 16% time for delegators than that of EOS, whereas under high bandwidth conditions (1000MBps), Siniel saves about 80% than EOS.
Natalia Borgoñós García, María Hernández Padilla, Antonio Fernando Skarmeta Gómez
Abstract Data Spaces are ecosystems designed to allow multiple organizations or companies to share data in a secure manner. Despite the potential of these technologies, they encounter a number of challenges and privacy issues that limit their use. Privacy Preserving Enablers are mechanisms developed to tackle these difficulties, ensuring data integrity and access control. This paper aims to analyze the role of some Privacy Preserving Enablers and its integration with Connectors in the context of Data Spaces. The research will focus on key enablers, including a Self-Sovereign Identity with Zero-Knowledge Proof, which is a privacy preserving approach that allows users to verify their identity and attributes without the need to disclose underlying data, ensuring their privacy. Additionally, the usage of Sticky Policies instantiated through Attribute-Based Encryption attaches control policies into the encrypted data in order to have an attribute-based access control, enhancing its security. The application of Policies Enforcement assure the consistent application of policies and the maintenance of the security within the Data Space.
Sharad Katkol, Praveen M. Dhulavvagol, Shashikumar G. Totad
Blockchain technology has the potential to transform healthcare data management by enhancing security, transparency, and data integrity. However, scalability, latency, and privacy concerns have limited its application in high-volume, sensitive environments such as healthcare. This paper introduces a blockchain architecture that addresses these challenges through adaptive sharding and rule-based data partitioning. The adaptive sharding algorithm dynamically adjusts shard configurations in response to real-time network demands, optimizing resource allocation and improving scalability. Meanwhile, rule-based data partitioning organizes transactions across shards based on specific attributes, such as transaction type or geographic region, to minimize cross-shard communication and improving processing efficiency. Together, these methods increase transaction throughput by 34% and reduce latency by 9% compared to traditional approaches. Additionally, the system incorporates Byzantine Fault Tolerance (BFT) consensus to strengthen security, along with zero-knowledge proofs and homomorphic encryption to protect sensitive patient data during transaction verification. This architecture provides a comprehensive, scalable, and secure blockchain solution tailored to the unique needs of healthcare data management, addressing critical limitations while maintaining privacy and data integrity.
With the advent of new sensor device designs, IoT based medical applications are increasingly being employed. This study introduces BlockFaaS: a Blockchain-assisted serverless framework that incorporates advanced AI models in latency sensitive healthcare applications with confidentiality, energy efficiency, and real-time decision-making. This framework combines the structure of AIBLOCK with dynamic sharding and zero knowledge proofs to make the framework strongly scalable with health-assured data inviolability with HealthFaaS, a serverless platform for cardiovascular risk detection. Explainable AI and federated learning models are introduced into the system to retain an equilibrium between data privacy and interpretability. All layers of communication use the Transport Layer Security protocol to ensure security. This proposed system is validated by new performance metrics such as real-time response rates and energy consumption, proving to be superior to the existing HealthFaaS and AIBLOCK technologies.
Faisal Alanazi, Mahdi Zareei, Alberto Rodríguez Arreola
The rapid growth of the Internet of Things (IoT) demands solutions that can secure massive streams of sensitive data without sacrificing performance. Traditional blockchains struggle in IoT environments, facing significant challenges with transaction speed, scalability, and privacy. This paper introduces PRIVOT, a novel blockchain architecture that integrates a Directed Acyclic Graph (DAG) for high-throughput consensus with lightweight zero-knowledge proofs (ZKPs) for confidential transactions, rateless coded computation for private analytics, and an AI-driven manager that dynamically balances security and efficiency. Our simulations show that PRIVOT significantly outperforms traditional blockchain approaches, achieving high transaction throughput (up to 480 TPS on a 500-device network) with confirmation latencies under 2.1 seconds, even under heavy load. The framework provides robust privacy, limiting data leakage to less than 0.1% against significant node collusion, while keeping computational overhead low enough for resource-constrained IoT devices. By unifying these techniques, PRIVOT offers a scalable and resilient solution ideal for large-scale IoT deployments where both high performance and strong privacy are paramount.
The Industrial Internet of Things (IIoT) generates a vast volume of sensitive data that demands not only confidentiality but also authenticity and integrity—especially in large-scale deployments. Ensuring that data originates from trusted devices is critical; however, existing authentication mechanisms often lack scalability and effective revocation support. To address these challenges, we proposeLightPUF-IIoT, a secure and lightweight authentication scheme designed for fog-assisted IIoT data sharing. The scheme leveragesPhysical Unclonable Functions (PUFs)andNon-Interactive Zero-Knowledge Proofs (NIZKPs)to enable scalable, group-based authentication for devices and fog nodes. By binding authenticated identities to cryptographic tokens used during data transmission, LightPUF-IIoT ensures data authenticity and supports real-time rogue device detection. The scheme also includes efficient mechanisms for device revocation and secure token regeneration. Experimental results show that LightPUF-IIoT provides strong security guarantees with minimal resource overhead and significantly outperforms existing approaches in terms of computational cost, scalability, and authentication throughput.
Optimistic Rollup is a blockchain scalability solution. However, it assumes that transactions are correct unless challenged by a challenger, which leads to the concept of a challenge period. If there is no challenge during this time, the user needs to wait until the end of the challenge period, which is very inconvenient for the user. This is because challengers and task publishers are self-interested and aim to maximize their own benefits, leading to internal incentive issues within Optimistic Rollup. Although zero-knowledge proofs can effectively address this issue, ZK rollups face challenges such as high computational complexity and the need for transaction-specific proofs. Optimistic rollups have low computational requirements and simple design, making the design of an effective incentive mechanism a good choice. This paper proposes a reward-based incentive model to encourage rational workers to actively verify transactions based on game theory. The rewards in this incentive model are related to a time factor, where earlier verification yields greater rewards, motivating verifiers to verify transactions more quickly. We simulate under various proportions of false declarations and the results show that our method could incentivize the workers effectively to exert their best efforts.
Secure personal data sharing remains a critical challenge in decentralized systems due to concerns over privacy, compliance, and trust. This paper presents the formal verification of a Blockchain-Based Security Model (BSM) designed to address these challenges through a multi-layered architecture. The proposed model integrates Chaincode-as-a-Service (CCaaS) on Hyperledger Fabric to ensure modular, maintainable, and scalable execution of smart contracts. A Flask-based API serves as the secure gateway for data operations and identity management. Sensitive data is stored off-chain using InterPlanetary File System (IPFS), preserving decentralization while minimizing on-chain bloat. Access control is enforced using efficient cryptographic techniques, while Intel SGX (or simulated enclaves) safeguards secure data processing and decryption within trusted execution environments. To further enhance privacy guarantees, Zero-Knowledge Proofs (ZKPs) are optionally integrated to enable verifiable claims without disclosing raw data. For assurance of correctness and security, the BSM is formally modeled using the Dolev-Yao attacker model and verified through ProVerif, focusing on key security properties such as confidentiality, integrity, authentication, and accountability. The findings confirm that the proposed model satisfies stringent security goals and is robust against symbolic adversaries. This work contributes a verifiable and extensible framework for privacy-preserving data sharing in sectors such as healthcare, finance, and government. To the best of our knowledge, this is among the first works to formally verify a blockchain-based security model that simultaneously integrates modular chaincode execution (CCaaS), trusted hardware enclaves (Intel SGX), decentralized off-chain storage (IPFS), and optional Zero-Knowledge Proofs (ZKPs) with a unified framework for personal data sharing.
U Parthiban, Vandhana Devi Pannerselvam, Ashok Murugesan, Kumar Ramasamy
Women still face persistent financial inclusion, employment verification, and wage transparency issues to their economic empowerment.Conventional hiring and financial systems impose disproportionate limitations on women to access credit and fair wages.ShePowerChain is a blockchain platform that aims to address these problems with decentralized finance, smart contracts, and verifiable credentials.ShePowerChain's secure, transparent, and automated transactions not only streamline processes but, depending on modeled scenarios, help reduce wage payment delays by 25% and increase women-led microloan access by 40%.By enabling secure, transparent, and automated transactions, ShePowerChain disintermediates, enhances job verification, and enforces wage fairness.The platform uses zero-knowledge proofs for privacy, multi-factor authentication for security, and Layer 2 scaling solutions for efficiency.While the results were from simulations and comparisons, they were not due to full real world usage.Comparative studies point to its potential to improve financial access, close wage gaps, and establish trust in hiring processes.The design also considers a serious ethical risk of excluding women who have low digital literacy, suggesting digital skills training, and adaptive strategies for community.Despite obstacles related to regulatory implications, and lack of digital literacy, blockchain provides an opportunity to facilitate and scale sustainable solutions to gender inclusive economic empowerment.
Brugeres, Maxence, Languille, Victor, Kuznetsov, Petr, Zarfaoui, Hamza
We propose a decentralized asset-transfer system that enjoys full privacy: no party can learn the details of a transaction, except for its issuer and its recipient. Furthermore, the recipient is not aware of the sender’s identity. Our system does not rely on consensus or synchrony assumptions, and therefore, it is responsive, since it runs at the actual network speed. Under the hood, every transaction creates a consumable coin equipped with a non-interactive zero-knowledge proof (NIZK) that confirms that the issuer has sufficient funds without revealing any information about her identity, the recipient’s identity, or the payment amount. Moreover, we equip our system with a regulatory enforcement mechanism that can be used to regulate transfer limits or restrict specific addresses from sending or receiving funds, while preserving the system’s privacy guarantees. Finally, we report on PaxPay, our implementation of Fully Private Asset Transfer (FPAT) that uses the Gnark library for the NIZKs. In our benchmark, PaxPay exhibits better performance than earlier proposals that either ensure only partial privacy, require some kind of network synchrony or do not implement regulation features. Our system thus reconciles privacy, responsiveness, regulation enforcement and performance.