Blockchain Integration With Multimodal Biometric Authentication System for Secure Smart Verifiable Electronic Voting System

The prevalence of political interference during election processes remains a significant challenge to the free, fair, and credible conduct of general elections. This election event is crucial as a pillar of democratic governance in any democracy, with the potential for unfitting disturbance and chaos, such as multiple votes, illicit voting, and malicious actors that can be exploited electronically to disrupt voting or affect vote counts. However, this research proposed to develop a secure, smart, verifiable e-voting system (SSVEVS) that can offer an authenticated end-to-end tally voting system, a top-secret ballot election system, and confidence in overall election integrity. The proof of this smart and secure electronic voting system utilizes a 64-bit quad-core ARM Cortex-A76 processor, integrated with multimodal biometric (facial and fingerprint) authentication systems, programmed with a deep-learning image processing (DLIP) algorithm to optimize image detection and recognition. Also, an Ethereum blockchain technology (EBT) with a homomorphic encryption algorithm was implemented on the system to ensure that the original information record is maintained, immutable, tamper-resistant, and transparent throughout the electoral process, and stores the information in a decentralized database application. The system achieved 1,424.501 TPS for 10,000 transactions with a mining time of 7.02 seconds. The biometric (facial and fingerprint) authentication achieved a False Acceptance Rate (FAR) and False Rejection Rate (FRR) of 0.00% respectively. The True Acceptance Rate (TAR) is 100%, with a false image template of 0.04% true identification rate (TPIR).

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3618970
Primary Topic
Internet Traffic Analysis and Secure E-voting
Type
article
Field-Weighted Citation Impact
0.00
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article

Blockchain Integration With Multimodal Biometric Authentication System for Secure Smart Verifiable Electronic Voting System

Ebenezer Esenogho, Buhari Ugbede Umar, Eustace M. Dogo, Modisane Cameron et al.
IEEE Access
Internet Traffic Analysis and Secure E-voting
article

Blockchain Integration With Multimodal Biometric Authentication System for Secure Smart Verifiable Electronic Voting System

Ebenezer Esenogho, Buhari Ugbede Umar, Eustace M. Dogo, Modisane Cameron, Henry Ohiani Ohize, Lukman Adewale Ajao
article en

Abstract

The prevalence of political interference during election processes remains a significant challenge to the free, fair, and credible conduct of general elections. This election event is crucial as a pillar of democratic governance in any democracy, with the potential for unfitting disturbance and chaos, such as multiple votes, illicit voting, and malicious actors that can be exploited electronically to disrupt voting or affect vote counts. However, this research proposed to develop a secure, smart, verifiable e-voting system (SSVEVS) that can offer an authenticated end-to-end tally voting system, a top-secret ballot election system, and confidence in overall election integrity. The proof of this smart and secure electronic voting system utilizes a 64-bit quad-core ARM Cortex-A76 processor, integrated with multimodal biometric (facial and fingerprint) authentication systems, programmed with a deep-learning image processing (DLIP) algorithm to optimize image detection and recognition. Also, an Ethereum blockchain technology (EBT) with a homomorphic encryption algorithm was implemented on the system to ensure that the original information record is maintained, immutable, tamper-resistant, and transparent throughout the electoral process, and stores the information in a decentralized database application. The system achieved 1,424.501 TPS for 10,000 transactions with a mining time of 7.02 seconds. The biometric (facial and fingerprint) authentication achieved a False Acceptance Rate (FAR) and False Rejection Rate (FRR) of 0.00% respectively. The True Acceptance Rate (TAR) is 100%, with a false image template of 0.04% true identification rate (TPIR).

IEEE AccessVol. 13
University of South Africa (ZA), Confluence Life Sciences (United States) (US), Federal University of Technology Minna (NG)
Openalex Percentile: Top 85%
Internet Traffic Analysis and Secure E-voting
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