Blockchain Papers

Dharmesh Kumar Sonkar, Sarvpal Singh, Amrendra Singh Yadav

No abstract is available for this record.

Henry Ohiani Ohize, Adeiza James Onumanyi, Lukman Adewale Ajao, Buhari Ugbede Umar · 9 authors

Despite significant advances in electronic voting technologies, voter accreditation in many electoral systems remains vulnerable to identity fraud, database tampering, equipment failure, and centralized security breaches. Existing accreditation solutions often rely on single-modal biometric authentication and centralized architectures, limiting their robustness, transparency, and public trust. This paper proposes a Blockchain-based Bimodal Voter Accreditation System (Block-BVAS), together with a practical framework for its deployment in electronic voting systems. The proposed system integrates multimodal biometric authentication using facial and fingerprint recognition with a private Ethereum blockchain and conventional cryptographic mechanisms to provide secure, tamper-resistant, and auditable voter accreditation to provide secure, decentralized, and tamper-resistant voter accreditation. A Raspberry Pi 5 serves as the embedded processing platform, demonstrating the feasibility of implementing the framework on cost-effective hardware. By combining distributed-ledger technology with encrypted biometric verification, the proposed architecture enhances the integrity, confidentiality, and immutability of election-related records while addressing limitations associated with single-factor authentication and conventional centralized record management. Experimental evaluation of the biometric authentication module performed effectively, with fingerprint recognition achieving an average authentication accuracy (AA) of 97.8% and facial recognition averaging 95.1%. The blockchain storage overhead (BSO) displayed a near-linear growth pattern relative to the number of transactions, consistent with theoretical expectations for blockchain architectures. Reliability analysis indicated system uptime exceeding 95%, with only minimal operational failures recorded during the test period. This blockchain implementation further demonstrated reliable transaction processing and secure record management, indicating the effectiveness of the proposed Block-BVAS in enhancing the security, transparency, and trustworthiness of electronic voter accreditation.

Abhay Kumar Yadav, Virendra P. Vishwakarma

No abstract is available for this record.

Saad Altamimi, Saad Alahmari, Ibrahim Alghamdi, Yousef Alhaizaey · 5 authors

Today, biometric authentication has become a central component of user security in social governance systems, where each government department demands access to user-specific data that varies across agencies. However, storing such data in centralized repositories increases serious privacy concerns, as unrestricted access by multiple entities maximizes the risk of data leakage. To address this, our research presents a novel biometric authentication system integrating robust privacy-preserving techniques, built on advanced deep learning architectures and differential privacy algorithms. A blockchain ledger integrated with a Merkle tree is used to securely store user identities, providing tamper-evident cryptographic validation of registered users. We further develop a novel hybrid model by integrating a pre-trained Vision Transformer (ViT) with a differential privacy-based machine learning enhanced training strategy, wherein the model is trained on noise-induced images to resist inference attacks. The system without differential privacy achieves 90.80% accuracy, 0.94 precision, 0.91 recall, and an F1-score of 0.90 in the standard configuration, while the differentially private model maintains 68.97% accuracy with ε = 6.2, ensuring a strong privacy—accuracy balance. The evaluation confirms that our proposed model, incorporating differential privacy, provides a secure and scalable solution for managing sensitive citizen data while achieving reliable performance in privacy-aware biometric verification for real-world e-governance applications.

Anithalakshmi V¹, Raja P², N Sripriya, M Lavanya

Abstract Traditional Identity and Access Management (IAM) systems rely on static credentials and centralized authorities, leaving organizations vulnerable to single points of failure, credential theft, insider misuse, and increasingly sophisticated deepfake impersonation attacks. In this paper, we propose a Decentralized AI-powered Zero-Trust IAM (DAZT-IAM) framework that combines permissioned blockchain infrastructure, self-sovereign identity (SSI) principles, and deepfake-resistant multimodal biometric authentication (face, voice, and behavioral keystroke dynamics) with a continuous, risk-adaptive AI trust scoring engine. The proposed system is compliant with ZTA principles and checks every access request continuously, unlike the older “authenticate-once” models. Blockchain-anchored Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) eliminate the dependency on a central identity provider. The biometric pipeline includes a dedicated deepfake-detection module that employs frequency-domain artifact analysis and temporal consistency checks to counteract synthetic media spoofing. We describe the system architecture, consensus and smart-contract design, the multi-modal fusion and liveness detection pipeline, and a risk-scoring model for adaptive access decisions. The experimental evaluation on simulated and benchmark datasets demonstrates that the proposed framework provides competitive authentication accuracy, high detection rates of deepfake attacks, and low average access decision latency, while removing single points of failure for identity. Our results demonstrate that the integration of blockchain-based decentralization and AI-based continuous trust evaluation provides a pragmatic approach of resilient and privacy-preserving IAM for sustainable digital infrastructure.

Dharmesh Kumar Sonkar, Sarvpal Singh, Amrendra Singh Yadav

No abstract is available for this record.

Hui Cui, Haoze Cheng, James Boorman

Biometric authentication offers enhanced usability for digital payments, but traditional centralized architectures suffer from single points of failure. While blockchain integration promises decentralized trust, existing solutions often store biometric helper data (e.g., fuzzy commitments) directly on-chain. We demonstrate that such transparency, even in permissioned settings, exposes low-entropy biometric inputs to offline brute-force attacks and identity-linkage risks if the immutable ledger is accessed by malicious nodes. To resolve this transparency–privacy paradox, we propose a threshold-based decentralized authentication framework. Unlike prior works, our protocol keeps all sensitive Biometric-Enhanced Key Derivation (BEKD) tokens entirely off-chain, using the blockchain solely for freshness enforcement. We provide a game-based security analysis of brute-force resistance, unforgeability, and unlinkability. Our experimental results demonstrate that our scheme’s gas cost is acceptable, offering a robust solution for self-sovereign biometric identity.