Follow blockchain research across journals, conferences, and preprint repositories.
8,484 results · page 56 of 354
Nouhaila Hanbali, Ahmed El-Yahyaoui
No abstract is available for this record.
Subhasis Thakur, John G. Breslin
No abstract is available for this record.
Y Zheng, Jianming Lin, Chang‐An Zhao
Abstract Bilinear pairings have emerged as a fundamental tool in public-key cryptography, enabling advanced protocols such as identity-based encryption, short signatures, and zero-knowledge proofs. This paper focuses on optimizing pairing computations on curves with embedding degree 2, addressing both theoretical foundations and practical implementations. We propose an optimized double-and-add ladder algorithm that leverages the technique of y -coordinate recovery, achieving superior performance for the Tate pairing on supersingular curves and the Omega pairing on non-supersingular curves. Our method is implemented based on the RELIC cryptographic library, demonstrating significant efficiency improvements over Miller’s algorithm. Specifically, it reduces the number of base field multiplications (respectively CPU clock cycles) by 17.53 % (respectively 13.58 %) for the reduced Tate pairing on supersingular curves with a 1536-bit field size and by 12.37 % (respectively 8.39 %) for the Omega pairing on non-supersingular curves of the same size. This work establishes the first comprehensive implementation framework for cubical-based pairing computations on curves with embedding degree 2, providing quantified optimizations for practical cryptographic deployment.
Yilai Lian, X. Liu, Lanying Liang, Wei Ye · 10 authors
No abstract is available for this record.
Dongyu Cao, Bixin Li, Huijie Zhang, Yong Wang · 5 authors
Blockchain technology improves supply chain management by ensuring the immutability of transaction records and facilitating process tracking. However, the transparency of blockchain raises significant privacy concerns, as sensitive information such as buyer and supplier qualifications, product specifications, and transaction amounts is often exposed. Compliance verification, which needs access to specific sensitive data for compliance checks, becomes challenging in blockchain-based privacy-preserving supply chains. This paper introduces ZKVeil, an innovative scheme utilizing zero-knowledge proof technology to maintain the confidentiality of sensitive information while ensuring compliance verification. Additionally, ZKVeil uses decentralized identifiers and verifiable credentials to ensure the authenticity of transaction data. A theoretical security analysis demonstrates the effectiveness of ZKVeil in safeguarding real sensitive data and ensuring compliance with regulations. To evaluate the performance of our scheme, we implement ZKVeil on a private blockchain of 100 nodes. Taking the shipbuilding supply chain transaction as an example, the experimental results demonstrate that ZKVeil incurs low gas consumption, execution time, and memory overhead.
Vengatagiri Gurumani
No abstract is available for this record.
Vignesh Kumar N., Petchiammal M.
The credibility of digital evidence is a cornerstone of modern cybercrime investigations, digital forensics, and judicial processes. However, adversarial tampering, deepfake manipulation, and insider threats have raised significant concerns regarding the authenticity and admissibility of such evidence. Conventional integrity-preservation methods—such as hashing, encryption, and secure storage—struggle to meet the demands of scalability, transparency, and resilience in today’s forensic environments. Recent advances in artificial intelligence (AI) and blockchain offer promising avenues for overcoming these limitations. AI techniques contribute to content-level verification by detecting anomalies, forgeries, and manipulations in digital artefacts, while blockchain ensures tamper-proof chain-of-custody management through decentralization, immutability, and auditability. This review synthesizes the state of the art in digital evidence integrity verification through the combined application of AI and blockchain. We examine existing frameworks, datasets, algorithms, and deployment models, while critically analyzing their strengths and limitations. Furthermore, we identify gaps in scalability, explainability, and legal admissibility, proposing future directions such as federated learning, explainable AI, zero-knowledge proofs, and quantum-resistant blockchains. By consolidating research across computer science, law, and digital forensics, this review highlights the potential of AI–blockchain synergy to establish robust, scalable, and trustworthy evidence verification frameworks for real-world forensic and judicial systems.
Yang Liu (4829), Linman Li, Jinchuan Chen
No abstract is available for this record.
Xinqi Dong, Yijia Wang, Yijia Wang, Xu Zhang · 6 authors
No abstract is available for this record.
Tanmayi Jandhyala, Guang Gong
No abstract is available for this record.
Hyeonbum Lee, Seunghun Paik, Hyunjung Son, Jae Hong Seo
An inner product argument (IPA) is a cryptographic proof system that serves as a fundamental building block for various applications, such as zero knowledge proofs and verifiable computation. Bulletproofs (IEEE S&P 2018), a well-known IPA under the discrete logarithm (DL) assumption, features a short, logarithmically-sized proof, making it suitable for blockchain applications. However, its major drawback is the linear verifier cost (O(N)), which presents a significant bottleneck in settings like verifiable computation. To address this, recent advancements have successfully reduced the verification complexity to square-root order (O(√N)) under the same assumption (e.g., Asiacrypt 2022, IEEE TIFS). In thiswork, we propose Cougar, a novel IPAthat breaks this square-root barrier to achieve an unprecedented cubic-root verifier complexity (O(3√N)), while strictly maintaining the compact logarithmic proof size (O(logN)) characteristic of Bulletproofs. To achieve this, Cougar introduces a generalized two-tier commitment framework combined with adisjoint interpolationstrategy for efficient consistency checks. We implemented Cougar in Rust and performed a comprehensive benchmarking against Bulletproofs and Leopard (IEEE TIFS). Our evaluation demonstrates that while Cougar incurs a moderate increase in prover overhead, its verification time scales significantly better for large instances. Concretely, for a witness size ofN= 220, Cougar achieves a 50× verification speed-up over Bulletproofs and exhibits a superior asymptotic growth rate compared to existing sublinear IPAs.
Tolibjon Mansurov, Bobir Boltaev, Khumoyun Barоtov, Makhliyo Sharifova · 5 authors
No abstract is available for this record.
Anshika Malsaria, Pankaj Vyas
No abstract is available for this record.
Gourav Singh, Arjun Pataskar
The integrity of electoral systems is fundamental to democratic governance; however, traditional voting mechanisms suffer from security vulnerabilities, lack of transparency, and accessibility constraints. This paper proposes a blockchain-based voting system leveraging distributed ledger technology to ensure secure, transparent, and tamper-resistant elections. The system integrates cryptographic techniques such as Zero-Knowledge Proofs (ZKPs) and Elliptic Curve Cryptography (ECC) within a permissioned blockchain framework using Hyperledger Fabric and Practical Byzantine Fault Tolerance (PBFT) consensus. A three-tier architecture consisting of Application, Blockchain, and Data Storage layers ensures scalability and efficiency. Security mechanisms including multi-factor authentication, end-to-end encryption, and AI-based anomaly detection mitigate potential threats such as Sybil attacks and denial-of-service attacks. Comparative analysis indicates improved security, transparency, and cost-effectiveness over traditional systems. The proposed framework demonstrates strong technical feasibility and provides a foundation for future advancements in digital electoral systems.
Chiang Liang Kok, Jie Heng, Brendon Choo, Nicholas Tan · 5 authors
No abstract is available for this record.
Priyanga K. K, Josheena Jose
No abstract is available for this record.
Laura Atmanavičiūtė, Mykolas Rutkauskas, Aristidas Lukas Končius, Gintarė Košubienė · 5 authors
No abstract is available for this record.
CHRISTIAN RODRIGUES PEREIRA
No abstract is available for this record.
Michael Schallop
No abstract is available for this record.
Sami Rashid Mohammed Shibah
No abstract is available for this record.
Haoyang Gao, X Wang
With its decentralized, tamper proof, transparent and traceable characteristics, blockchain technology has shown great potential in fields such as finance, supply chain, and the Internet of Things. However, the public transparency of its ledger poses a serious challenge to user transaction privacy. Traditional privacy protection schemes such as homomorphic encryption and zero knowledge proofs can enhance privacy, but often struggle to balance computational overhead, communication costs, and data availability. This article explores the innovative application of neural networks in blockchain privacy protection and proposes a transaction obfuscation model based on generative adversarial networks. This model utilizes a generator to learn the statistical features of raw transactions and generate difficult to track obfuscated transactions, while ensuring the validity and compliance of obfuscated transactions through discriminators and blockchain verification contracts. The experimental results show that compared with traditional obfuscation methods and differential privacy methods, the proposed model significantly reduces the consumption of privacy budget and computation delay while ensuring high transaction utility (such as reducing address correlation by more than 85%), achieving a better balance between privacy and utility. This study provides new ideas for building efficient and practical blockchain privacy enhancement solutions.
Jean Claude Niyokwizerwa
No abstract is available for this record.
Corentin Jeudy, Olivier Sanders
No abstract is available for this record.
Jayesh J. Gamar
No abstract is available for this record.