Exploring the Synergy of Blockchain and Federated Learning in Online Voting Systems

By: Himanshu Tiwari, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, nomails1337@gmail.com

Traditional online voting systems face security, privacy, and trust issues in the digital age. This innovative study uses blockchain and federated learning to address these challenges. We want to use blockchain’s immutability and federated learning’s decentralised learning model to strengthen online voting systems’ security, transparency, and privacy.

Traditional online platforms are vulnerable to cyberattacks and data integrity concerns, making voting system security crucial. Blockchain’s immutable, decentralised ledger protects against fraud and tampering. We aim to restore public trust in digital voting by integrating blockchain technology into online voting to ensure vote security.

Meanwhile, federated learning adds a unique element to our solution. This decentralised machine learning method lets models train on distributed datasets across devices while protecting voter data. Federational learning protects citizens’ personal data when voting online, which is especially vulnerable to data privacy concerns. Our research uses these technological advances to improve online voting security, transparency, and privacy. This project could transform digital democracy by building trust and security and empowering citizens to vote with confidence.

1. Introduction:

Online voting has revolutionised voting by making it more accessible and convenient. However, security, voter privacy, and ballot integrity remain issues. The transparent, tamper-proof blockchain, a decentralised, immutable ledger, may solve the problem.

Federated Learning (FL), the rising star of decentralised machine learning, lets models learn from distributed datasets on multiple devices, protecting data privacy. In this paper, blockchain’s security and FL’s privacy-preserving capabilities are combined to create a secure and private online voting system that revolutionises democracy for the digital age.

2. Background and Related Work:

2.1. Blockchain in Online Voting:

Blockchain technology enables secure online transactions, which are applicable in voting systems. Several studies have explored blockchain-based voting systems, focusing on the technology’s ability to provide transparent yet anonymous transactions, ensuring the voter’s privacy and the vote’s integrity. However, these systems still face challenges in scalability and voter identity verification.

2.2. Federated Learning:

Federated learning involves training an algorithm across multiple devices holding local data samples without exchanging them. This method ensures data privacy, a critical aspect of personal data involved in voting systems. However, its application in online voting is relatively unexplored.

3. Proposed System:

We propose an online voting system using blockchain to handle vote transactions and federated learning to manage voter data and identity verification, ensuring a secure and private voting process.

3.1. System Architecture:

The system architecture comprises several nodes participating in the voting process, each representing a voter. These nodes collectively form a blockchain network, responsible for recording voting transactions. The FL server, distinct from the blockchain network, is responsible for voter identity verification, utilizing a federated learning model trained with voters’ data without extracting the data from their local devices.

3.2. Voting Process:

The voting process involves identity verification, vote casting, and vote tallying. The FL model verifies voter identity, after which the verified voter casts their vote. This transaction, encrypted and secure, is recorded on the blockchain. Upon completion of the voting process, the votes are tallied through smart contracts, ensuring transparency and immutability.

4. Security and Privacy Analysis:

Integrating blockchain with federated learning enhances the system’s security and privacy. Blockchain’s decentralization and consensus algorithms eliminate the risks of data manipulation and ensure data integrity. Concurrently, FL’s decentralized data approach ensures voter identity and data remain private, addressing data security concerns prevalent in traditional online voting systems.

5. Challenges and Future Work:

While the proposed system addresses security and privacy concerns, several challenges arise, including system scalability, the complexity of integration, and real-time processing. Future research must explore optimization techniques, possibly through sharding or state channels, to enhance transaction processing and system scalability. Additionally, robustness against adversarial attacks in FL must be a research priority.

6. Conclusion:

This study underscores the potential of integrating blockchain and federated learning in online voting systems, promoting a secure, transparent, and privacy-preserving voting environment. While challenges remain, this synergistic approach marks a significant stride toward revolutionizing online voting systems, potentially influencing various other domains requiring secure and private data transactions.


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Cite As

Tiwari H. (2023) Exploring the Synergy of Blockchain and Federated Learning in Online Voting Systems, Insights2Techinfo, pp.1

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