Enhanced Information Security for Voting System in Emergencies Using Paillier’s Cryptosystem
Year: 2025
Type: article
Abstract: This study explores the application of Paillier’s Partial Homomorphic Encryption (PHE) in the context of secure digital voting systems, particularly in emergency situations such as pandemics, natural disasters, or martial law. The proposed system is implemented using Python with the Django framework and the pycryptodome library to ensure a secure and scalable environment. A key feature of Paillier’s cryptosystem is its ability to perform computations directly on encrypted data, which preserves voter confidentiality and guarantees data integrity without requiring decryption. A simulated voting scenario involving 10 voters and 3 candidates was conducted to evaluate the system. Encrypted votes were processed using homomorphic operations, allowing for the secure aggregation of votes. The results demonstrated that the system accurately computed vote totals—35 votes for Candidate A, 50 for Candidate B, and 100 for Candidate C—without compromising security. The system proved efficient and reliable for small-scale implementations. However, the study identifies significant challenges when scaling the system to national-level elections. The cryptographic operations required by Paillier’s scheme are computationally intensive and could hinder performance when processing millions of encrypted votes in real-time. Therefore, while the system shows high potential for secure e-voting, further research is required to optimize performance. The authors propose future work in two directions: optimizing the underlying cryptographic operations and integrating blockchain technologies to enhance transparency and auditability. Overall, the results suggest that Paillier’s PHE provides a robust framework for emergency e-voting systems and offers a substantial improvement over traditional voting methods in terms of both security and privacy.
DOI: 10.37943/22fula2667
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412899266