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.