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

2025-08-07

AI Summary

To address practical bottlenecks in blockchain-based e-voting systems—namely scalability limitations, high computational overhead, and insufficient privacy guarantees—this paper proposes a novel architecture integrating large language models (LLMs). Methodologically, it introduces a lightweight hybrid consensus mechanism and efficient cryptographic protocols; develops an LLM-powered framework for automated smart contract generation, formal verification, and anomaly detection; and incorporates decentralized identity management with natural-language user interfaces. Its key contribution is the first deep integration of LLMs across the entire voting lifecycle—from contract development and security validation to end-user interaction. Experimental evaluation on a national-scale simulation demonstrates that the end-to-end prototype achieves a 3.2× throughput improvement, reduces smart contract vulnerability rates by 87%, and preserves censorship resistance, verifiability, and usability—thereby offering a deployable technical pathway toward large-scale, trustworthy decentralized voting.

Technology Category

• Humans and AI: Voting
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Security and Privacy: Blockchains and distributed ledgers

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.

Problem

• Analyzing blockchain E-Voting architectures and consensus mechanisms
• Addressing scalability and privacy challenges in blockchain voting
• Exploring LLMs for smart contract generation and validation

Innovation

• Hybrid consensus for blockchain scalability
• Lightweight cryptography for efficiency
• LLM-guided smart contract generation