Blockchain-based Voting Systems: Studying blockchain-based voting systems for secure, transparent, and tamper-resistant electronic voting in elections and referendums

Authors

  • Dr. Ekaterina Ovchinnikova Associate Professor of Applied Mathematics and Computer Science, Saint Petersburg State University, Russia Author

Keywords:

Blockchain, Electronic Voting, Security, Transparency, Tamper Resistance

Abstract

Blockchain technology has garnered significant attention for its potential to revolutionize various industries, and one area where it holds particular promise is in voting systems. This paper explores the use of blockchain for electronic voting, focusing on its ability to enhance security, transparency, and tamper resistance in elections and referendums. We examine the underlying principles of blockchain technology, its application to voting systems, and the benefits it offers over traditional methods. Additionally, we discuss the challenges and limitations of implementing blockchain-based voting systems and propose recommendations for future research and development in this field.

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References

Perumalsamy, Jegatheeswari, Bhargav Kumar Konidena, and Bhavani Krothapalli. "AI-Driven Risk Modeling in Life Insurance: Advanced Techniques for Mortality and Longevity Prediction." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 392-422.

Karamthulla, Musarath Jahan, et al. "From Theory to Practice: Implementing AI Technologies in Project Management." International Journal for Multidisciplinary Research 6.2 (2024): 1-11.

Jeyaraman, J., Krishnamoorthy, G., Konidena, B. K., & Sistla, S. M. K. (2024). Machine Learning for Demand Forecasting in Manufacturing. International Journal for Multidisciplinary Research, 6(1), 1-115.

Karamthulla, Musarath Jahan, et al. "Navigating the Future: AI-Driven Project Management in the Digital Era." International Journal for Multidisciplinary Research 6.2 (2024): 1-11.

Karamthulla, M. J., Prakash, S., Tadimarri, A., & Tomar, M. (2024). Efficiency Unleashed: Harnessing AI for Agile Project Management. International Journal For Multidisciplinary Research, 6(2), 1-13.

Jeyaraman, Jawaharbabu, Jesu Narkarunai Arasu Malaiyappan, and Sai Mani Krishna Sistla. "Advancements in Reinforcement Learning Algorithms for Autonomous Systems." International Journal of Innovative Science and Research Technology (IJISRT) 9.3 (2024): 1941-1946.

Jangoan, Suhas, Gowrisankar Krishnamoorthy, and Jesu Narkarunai Arasu Malaiyappan. "Predictive Maintenance using Machine Learning in Industrial IoT." International Journal of Innovative Science and Research Technology (IJISRT) 9.3 (2024): 1909-1915.

Jangoan, Suhas, et al. "Demystifying Explainable AI: Understanding, Transparency, and Trust." International Journal For Multidisciplinary Research 6.2 (2024): 1-13.

Krishnamoorthy, Gowrisankar, et al. "Enhancing Worker Safety in Manufacturing with IoT and ML." International Journal For Multidisciplinary Research 6.1 (2024): 1-11.

Perumalsamy, Jegatheeswari, Muthukrishnan Muthusubramanian, and Lavanya Shanmugam. "Machine Learning Applications in Actuarial Product Development: Enhancing Pricing and Risk Assessment." Journal of Science & Technology 4.4 (2023): 34-65.

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Published

10-06-2024

How to Cite

[1]
D. E. Ovchinnikova, “Blockchain-based Voting Systems: Studying blockchain-based voting systems for secure, transparent, and tamper-resistant electronic voting in elections and referendums”, J. of Artificial Int. Research and App., vol. 4, no. 1, pp. 453–460, Jun. 2024, Accessed: Dec. 27, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/186

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