Follow blockchain research across journals, conferences, and preprint repositories.
9,005 results · page 37 of 376
Zhe Li, Chaoping Xing, Yizhou Yao, Chen Yuan · 5 authors
No abstract is available for this record.
Nam Tran, Khoa Nguyen, Dongxi Liu, Josef Pieprzyk · 5 authors
No abstract is available for this record.
Thomas den Hollander, Daniel Slamanig
No abstract is available for this record.
Shai Levin, Robi Pedersen
No abstract is available for this record.
Jens Groth, Harjasleen Malvai, Andrew Miller, Yi-Nuo Zhang
No abstract is available for this record.
Rohaila Naaz
In the contemporary digital landscape, the demand for secure, private, and tamper-resistant communication has never been more critical. Conventional messaging platforms, which predominantly rely on centralized servers, are increasingly vulnerable to data breaches, unauthorized surveillance, and censorship. Even with the adoption of end-to-end encryption, these systems remain susceptible to single points of failure and metadata exposure, undermining user privacy and trust. Blockchain technology has emerged as a transformative solution to these challenges, offering a decentralized, immutable, and transparent infrastructure for secure data exchange. By leveraging distributed ledger technology, Blockchain-based messaging systems eliminate the need for trusted intermediaries, enhance resistance to censorship, and ensure data integrity through consensus-driven validation.
Dinesh K, Uma Mahesh, T Naresh
Access to digital services requires entities, such as users or software services, to establish their identities before interacting with service providers. Conventional identity management systems typically maintain separate identity records for each application, often resulting in multiple accounts for the same entity within a single service provider. When identical personally identifiable information and attributes are reused across platforms, these fragmented records can be correlated, increasing the risk of identity exposure and privacy breaches. This work presents an entity-centric identity management model tailored for cloud environments, designed to enhance privacy and reduce unnecessary information disclosure. The proposed approach is founded on two core components. The first is anonymous identification, which enables entities to interact with cloud services based on predefined privacy preferences without revealing their true identities. The second component introduces active bundles, which encapsulate personally identifiable information, usage policies, and an embedded execution environment responsible for enforcing privacy constraints. These bundles autonomously apply protection mechanisms to safeguard sensitive data, even when deployed on untrusted platforms. The proposed model offers several advantages, including reduced dependence on external identity providers, controlled disclosure of identity attributes to service providers, and secure utilization of identity data in untrusted cloud environments. By integrating privacy-enhancing technologies such as zero-knowledge proofs, the framework provides a robust and flexible solution for privacy-aware identity management in modern cloud-based systems.
Mazari, Ilyes Tarik
This document provides a comprehensive prior art disclosure for the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The Y.I.N. Mazari Ordering establishes that for efficient cryptographic verification of differential privacy compliance, zero-knowledge proofs must be generated before encryption, not after. This disclosure documents extensions, variations, and applications of the ordering across: (1) all cryptographic primitives including post-quantum schemes, (2) all zero-knowledge proof systems, (3) diverse application domains including financial services, healthcare, and emerging technologies, and (4) various architectural configurations and trust models. The disclosure is published in the spirit of scientific contribution while establishing prior art for the described variations. Associated patent applications: U.S. Provisional Patent No. 63/923,348, U.S. Patent Application No. 19/399,646, and U.S. Continuation Application No. 19/403,244. Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Y.I.N. Mazari Ordering, Privacy-Preserving Machine Learning, Prior Art Disclosure
Hanlei Cheng, Sio‐Long Lo, Jing Lu
Keyword search is a fundamental technique for retrieving data outsourced to the cloud. Although encryption preserves data confidentiality, existing searchable encryption schemes often fail to efficiently support dynamic authorization and flexible retrieval. To address these limitations, we propose BAMKS , a blockchain-assisted attribute-based multi-keyword search scheme that supports secure and efficient search over version-aware encrypted data. In BAMKS , multiple data owners collaboratively generate version-bound access tokens that grant authorized users decryption privileges over evolving data. The scheme further enables conjunctive keyword search with updatable indexes. To ensure the integrity of search results, users can verify their correctness using an aggregated Schnorr-based non-interactive zero-knowledge proof, which is validated by smart contracts. In addition, BAMKS provides efficient attribute and user revocation without re-encrypting the stored ciphertexts, and supports user traceability for identifying malicious users from leaked keys. We formally prove that BAMKS achieves security against chosen-plaintext attacks (IND-CPA) and chosen-keyword attacks (IND-CKA) under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. Performance evaluations show that the scheme achieves lightweight decryption and efficient multi-keyword search, thereby reducing client-side computation and making it suitable for resource-constrained IoT environments. These features demonstrate the practicality of BAMKS for distributed cloud-edge-IoT storage applications.
Nandini K, Giris Shivappa, Sharon Zachariah, Thanushree B.Tech Thanushree B.Tech · 8 authors
Genomic data sharing remains a core problem in precision medicine because genomic data are highly sensitive and unchangeable. In this article, we propose a blockchain-based framework that utilizes zero-knowledge proofs (ZKPs), smart contracts, and off-chain storage to facilitate secure, privacy-preserving data sharing within health record systems. We implemented and evaluated a proof-of-concept prototype in Python on a simulated genomic dataset. The prototype uses a hybrid storage system where metadata is retained on a blockchain and encrypted data are placed in an emulated InterPlanetary File System (IPFS). Rule-based access is controlled using smart contracts, while privacy and security are achieved using ZKPs with interactive Schnorr protocol and elliptic curve cryptography (ECC). Empirical analysis using real-time testing over 100 iterations reported an average zero-knowledge proof with blockchain (ZKPB) query latency of 5.83 ms with a 90.00% accuracy, smart contract latency of under 0.01 ms with 90.00% accuracy, blockchain query time of 0.01 ms with 90.00% accuracy, and ECC latency of 8.72 ms with 90.00% accuracy. These empirical findings validate the effectiveness and privacy guarantees of the framework, which can be utilized in healthcare research, clinical genomics, and personalized medicine workflows.
Linkai Zhu, Xueyan Zhang, Zeyu Zhang, Di Wu · 5 authors
No abstract is available for this record.
Dan Wang, Ying Wang
Sample alignment performs a crucial role in vertical federated learning, aiming to identify shared user samples among multiple parties without exposing their private identifier data. However, most existing alignment protocols are designed for two-party scenarios, while those developed for multi-party settings suffer from limited anti-collusion capability and inefficient verification mechanisms. To address these issues, we propose an efficient and secure protocol for sample alignment in multi-party vertical federated learning (MESA). The protocol leverages a threshold oblivious pseudo-random function (T-OPRF) combined with a distributed key generation scheme to defend against collusion attacks. Moreover, an oblivious key–value store encoding (OKVS) mechanism is introduced to enable secure and efficient key–value mapping and decoding, thereby reducing communication overhead. Under the malicious security model, MESA further incorporates non-interactive zero-knowledge proof (NIZKP) to verify the consistency and validity of results submitted by clients, effectively preventing data forgery and disruption attacks. Experimental results and analysis demonstrate that MESA provides strong privacy guarantees while achieving high computation and communication efficiency in deployments involving multiple untrusted clients.
Mingxuan Chen, Puhe Hao, Weizhi Meng, Yasen Aizezi · 5 authors
No abstract is available for this record.
Igor, Chechelnitsky
This publication introduces Zero-Knowledge Behavioral Proof (ZKBP) as a post-biometric authentication primitive designed for the QADMON canonical security framework. ZKBP replaces traditional biometric and password-based identity with cryptographically verifiable behavioral continuity. The protocol proves liveness, integrity and continuity of behavior without revealing biometric templates, raw behavioral signals, or any permanent human identifier. The package includes: - Formal cryptographic definition of ZKBP - Security proofs under LWE-based post-quantum assumptions - Comprehensive threat model (AI imitation, replay, side-channels, insider threats) - Protocol specification in JSON - Comparative security tables (CSV) - Multilingual human-readable documentation (EN, RU, HE, ZH, AR) - Implementation notes for PQC + TEE environments This module follows the canonical QADMON axiom: FSIG ≠ Cryptographic Key FSIG = Zero-Knowledge Behavioral Proof The only cryptographic secret is a post-quantum key stored inside a Trusted Execution Environment (TEE). This work is published as Module 02 of the QADMON Canonical Security Framework.
Shalu Mishra, Rajendra Kumar Dwivedi
No abstract is available for this record.
Liuyu Yang, Xinxuan Zhang, Yi Deng, Zhuo Wu · 5 authors
No abstract is available for this record.
Yibin Yang
No abstract is available for this record.
Nicholas Tio, Octara Pribadi, Robet Robet
The increasing need for trustworthy digital document verification presents challenges in ensuring authenticity, transparency, and tamper resistance without relying on centralized authorities. This study aims to develop and evaluate a decentralized document notarization system using Ethereum and IPFS that offers secure, transparent, and cost-efficient verification. The system employs modular smart contracts deployed through a factory pattern to create user-specific verifier instances, enabling document submission, revocation, and verification using keccak-256 hashes, ECDSA signatures, and IPFS content identifiers. Methods include contract development, deployment on a local Hardhat network, performance benchmarking, and front-end integration for user interaction. Results show that verifier deployment consumes approximately 1.19 million gas (≈$85 at 20 gwei), document submission around 85 thousand gas (≈$6), and revocation about 50 thousand gas (≈$3.50). Client-side operations such as hashing and IPFS pinning occur in under 50 milliseconds, while real-world blockchain confirmations take 10–30 seconds. The findings demonstrate that decentralized notarization using Ethereum and IPFS is both technically feasible and economically viable. Future enhancements, including Layer 2 rollups, batch notarization, and privacy-preserving features such as encrypted IPFS pinning or zero-knowledge proofs, are proposed to further improve scalability, cost-efficiency, and data confidentiality
Dheerendra Mishra, Rohit Raj Sharma
No abstract is available for this record.
Mazari, Ilyes Tarik, Mazari, Yanis, Mazari, Ilyan
We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise → proof → encrypt → aggregate) is proven to be necessary—no efficient alternative exists—and universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning
Mazari, Ilyes Tarik, Mazari, Yanis, Mazari, Ilyan
We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise → proof → encrypt → aggregate) is proven to be necessary—no efficient alternative exists—and universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning
Helger Lipmaa
No abstract is available for this record.
Rajesh Sharma, Akey Sungheetha, S. Saranya, Vijayan Ellappan · 6 authors
Thus, the synergy of artificial intelligence (AI)-based technologies and digital financial transactions require secure anonymized methods while retaining the effectiveness of AI-based fraud-detection. This systematic review investigates stateof-the-art means of enhancing privacy assurance in ML by leveraging innovative schemes to safeguard money transfers in electronic platforms. Many privacy-preserving techniques are available and can be adopted by financial institutions to analyses encrypted data these include homomorphic encryption and federated learning. Employing these methods, AI models can identify fraudulent behavior patterns while at the same time not compromising on the privacy of single transactions. There is an extra level of security or anonymity given x by zero-knowledge proof which allows for the verification of the transactions without disclosing the data behind such transactions. Differential privacy is also used to apply noise on data to ensure that no distinguishing data set is used by the algorithm while ensuring the data is useful for statistical purposes for the ML models used. As much as its integration offers potential in carrying these privacy-shields presents some considerations. Mainly, they improve security and users’ confidence but at the same time introduce computation cost and system intricacy. This review therefore looks at different implementation strategies and hybrid solutions which employ several ideas aimed at maintaining high efficiency of the applied privacy-preserving techniques. Security: Advanced developments in hardware acceleration and algorithms have brought into use these methods nearer to real life applications. It also explores areas of future development including quantum protection of privacy and privacy preserving AI systems. Nonetheless, time and again there are instances where researchers experienced difficulties in the actual implementation such as the approaches may not be scalable, in other words may not well work for large data sets, or that there is need to standardize these models for privacy-preserving AI to be well embraced as it remains one of the most important revolutions by which the safety of financial systems in the digital world can be enhanced. As trading volumes increase and the regulation of how clients’ data is used gets stricter, these technologies will be at the heart of shielding consumer information whilst facilitating enhanced fight against fraud.
Wai Yie Leong
The accelerating digitalization of critical national infrastructures has underscored the urgent need for sovereign control over data, trust, and governance in cyberspace. Traditional centralized systems, while functional, are increasingly vulnerable to single points of failure, unauthorized access, and opaque accountability structures. Against this backdrop, blockchain technologies offer decentralized trust, immutable record-keeping, and programmable compliance mechanisms that can be embedded into national data infrastructures to reinforce digital sovereignty. This paper investigates how blockchain-backed architectures can serve as foundational enablers of sovereign control over data flows, policy enforcement, and audit transparency within a nation-state context. Using a multi-layered methodology that combines policy–technology mapping, comparative analysis of governance frameworks, and case studies across e-government, healthcare, and energy utilities, the study introduces a sovereignty-by-design blockchain framework tailored for Malaysia and ASEAN member states. Results demonstrate that blockchain-based infrastructures improve auditability by over 40%, reduce compliance latency by 35%, and enhance cross-border contractual assurance through integration with ASEAN Model Contractual Clauses (MCCs). The study also highlights the role of privacy-enhancing technologies (PETs) such as confidential computing and zero-knowledge proofs in aligning blockchain with personal data protection laws.