Secure and Scalable Blockchain Voting: A Comparative Framework and the Role of Large Language Models

• URL: http://arxiv.org/abs/2508.05865v1
• Date: Thu, 07 Aug 2025 21:34:21 GMT
• Title: Secure and Scalable Blockchain Voting: A Comparative Framework and the Role of Large Language Models
• Authors: Kiana Kiashemshaki, Elvis Nnaemeka Chukwuani, Mohammad Jalili Torkamani, Negin Mahmoudi,
• Abstract summary: This paper presents a comparative framework for analyzing blockchain-based E-Voting architectures, consensus mechanisms, and cryptographic protocols.<n>We propose optimization strategies that include hybrid consensus, lightweight cryptography, and decentralized identity management.<n>Our findings offer a foundation for designing secure, scalable, and intelligent blockchain-based E-Voting systems suitable for national-scale deployment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Blockchain technology offers a promising foundation for modernizing E-Voting systems by enhancing transparency, decentralization, and security. Yet, real-world adoption remains limited due to persistent challenges such as scalability constraints, high computational demands, and complex privacy requirements. This paper presents a comparative framework for analyzing blockchain-based E-Voting architectures, consensus mechanisms, and cryptographic protocols. We examine the limitations of prevalent models like Proof of Work, Proof of Stake, and Delegated Proof of Stake, and propose optimization strategies that include hybrid consensus, lightweight cryptography, and decentralized identity management. Additionally, we explore the novel role of Large Language Models (LLMs) in smart contract generation, anomaly detection, and user interaction. Our findings offer a foundation for designing secure, scalable, and intelligent blockchain-based E-Voting systems suitable for national-scale deployment. This work lays the groundwork for building an end-to-end blockchain E-Voting prototype enhanced by LLM-guided smart contract generation and validation, supported by a systematic framework and simulation-based analysis.

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