A Study on Optimization of Data Privacy Protection Algorithms Based on Distributed Artificial Intelligence Techniques

In the field of data privacy protection, data access is restricted by setting access privileges and authentication mechanisms. However, when data usage scenarios or access requirements change, the privilege settings need to be adjusted frequently, resulting in complex and inefficient management. Especially when dealing with large-scale distributed data, traditional methods are difficult to efficiently screen and eliminate malicious behavior clients, which further aggravates the risk of privacy leakage. Therefore, we study the optimization of data privacy protection algorithm based on distributed artificial intelligence technology. The key feature information is extracted to reflect the essential attributes of the data, in order to simplify the form of data representation. The federal learning algorithm in distributed artificial intelligence technology is used for data protection to efficiently screen and eliminate potential malicious clients. On this basis, we add appropriate amount of noise to the query process to protect the data from being leaked, and at the same time, dynamically adjust the data privacy protection algorithm and update the protection scheme in real time to cope with the changing nature of the data and its intrinsic relevance. The experimental results show that the optimization method of data privacy protection algorithm based on distributed artificial intelligence technology is significantly better than other comparative methods in reducing the possibility of privacy leakage, improving privacy protection efficiency, and reducing data loss rate. Moreover, the security and reliability of privacy data is as high as 90%, fully verifying the superiority of the proposed method. The results indicate that this method provides an efficient, stable, and secure technical means for data privacy protection.