A Data Sharing Privacy Protection Model Based on Federated Learning and Blockchain Technology

Abstract

As the main driving force for social development in the new era, data sharing is controversial in terms of privacy and security. Traditional privacy protection methods are a bit challenging when faced with complex and massive shared data. Given this, firstly, the Byzantine consensus algorithm in blockchain technology was elaborated. Meanwhile, a decision tree algorithm was introduced for node classification optimization, and a new consensus algorithm was proposed. In addition, local data training and updating were achieved through federated learning, and a new data-sharing privacy protection model was proposed after jointly optimizing consensus algorithms. The maximum throughput of the optimized consensus algorithm was 1560. The maximum consensus delay was 110 milliseconds. After multiple iterations, the removal rate of the Byzantine nodes reached 56.6%. The optimal reputation value of the new data-sharing privacy protection model was 0.75. The lowest reputation value after 10 iterations was 0.32. As a result, this proposed model achieves excellent results in data sharing privacy protection tasks, demonstrating high model feasibility and effectiveness. The research aims to provide a reliable method for data sharing privacy protection in the field.