VoteGuard: A Hybrid Blockchain-AI Framework for Secure Electronic Voting with Enhanced Biometric Authentication and Decentralized Integrity
Year: 2025
Type: article
Abstract: Contemporary electoral systems face an unprece-dented trilemma encompassing security vulnerabilities, trans-parency deficits, and accessibility constraints that compromise democratic integrity. This research presents VoteGuard, an innovative hybrid framework that addresses these challenges through the strategic integration of artificial intelligence-driven biometric authentication and blockchain-based decentralized ledger technology. The proposed architecture employs a novel “Centralized Orchestration of Decentralized Trust” paradigm, wherein TensorFlow.js-powered facial recognition with liveness detection mechanisms ensures robust voter authentication at the edge, while a permissioned Ethereum Sepolia Sepolia blockchain maintains immutable vote records through smart contract au-tomation. The system leverages cutting-edge technologies including TypeScript for type-safe development, Bun runtime for opti-mized performance, React.js for responsive user interfaces, and IPFS for decentralized biometric data storage. Comprehensive evaluation demonstrates exceptional performance metrics: 99.5% biometric authentication accuracy with sub-300ms processing latency, processing capacity exceeding 75,000 votes per second, and complete cryptographic immutability of electoral records. Security analysis reveals multi-layered defense mechanisms including AES-256 encryption, SHA-256 cryptographic hashing, and zero-knowledge proof protocols for privacy preservation. The architecture achieves full regulatory compliance with GDPR requirements through data anonymization and provides real-time audit capabilities while maintaining voter privacy. Comparative analysis against traditional and existing digital voting systems demonstrates significant superiority in security metrics, oper-ational efficiency, and voter confidence indicators, establishing VoteGuard as a foundational framework for next-generation democratic participation.
Author: Kedar Pinniboyina
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7083321591
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