VoteGuard: A Hybrid Blockchain-AI Framework for Secure Electronic Voting with Enhanced Biometric Authentication and Decentralized Integrity

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.

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-09-25
DOI
https://doi.org/10.22214/ijraset.2025.74239
Primary Topic
Agriculture, Water, and Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

VoteGuard: A Hybrid Blockchain-AI Framework for Secure Electronic Voting with Enhanced Biometric Authentication and Decentralized Integrity

International Journal for Research in Applied Science and Engineering Technology
Agriculture, Water, and Health
article

VoteGuard: A Hybrid Blockchain-AI Framework for Secure Electronic Voting with Enhanced Biometric Authentication and Decentralized Integrity

article en

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.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(9)
Decent work and economic growth
Openalex Percentile: Top 52%
Agriculture, Water, and Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

VoteGuard: A Hybrid Blockchain-AI Framework for Secure Electronic Voting with Enhanced Biometric Authentication and Decentralized Integrity · International Journal for Research in Applied Science and Engineering Technology (2025) | TGRS Research Map | TGRS