Research on Differential Privacy Protection of Power Grid Data Based on Artificial Intelligence and Federated Learning

With the rapid growth and intelligent development of power grid data, privacy protection of this data has become a pressing issue. This paper proposes a differential privacy protection method based on artificial intelligence and federated learning to address the privacy protection needs of power grid data. First, the basic concepts and related research results of differential privacy and federated learning are reviewed, and the advantages and disadvantages of existing power grid data privacy protection schemes are analyzed. Then, a privacy protection system combining differential privacy and federated learning technologies is designed and implemented, including modules for data processing, model training, and privacy protection mechanism integration. Experiments on real power grid datasets verify that this system can effectively improve the accuracy of data analysis and model training while ensuring data privacy. The experimental results show that this method can effectively protect the privacy of power grid data and perform well in various application scenarios. Finally, this paper discusses the prospects of this method in practical applications and future research directions.