关键词: 纵向联邦学习算法, 隐私数据, 随机森林回归模型, 复合混沌序列, 防篡改加密

Abstract: The security of private data is affected by internal and external threats, such as data leakage, malicious tampering, hacker attacks, etc. In the process of privacy data tamper proof encryption, the key issue is to ensure the integrity and security of data while maintaining its privacy. Therefore, an encryption method of privacy data tamper proof based on longitudinal federated learning algorithm is proposed. By using vertical federated learning algorithms to construct a hierarchical structure for network malicious user detection, malicious user detection is carried out to ensure the integrity and security of private data. Using a random forest regression model to accurately classify private data, and generating chaotic random sequences of private data using composite chaotic sequences. In the sequence encoding stage, a binary optimization method is adopted to ensure the efficiency and accuracy of the encoding. Encryption keys for private data is designed using key control methods, achieving tamper proof encryption of private data through feature clustering and encoding fusion techniques. The experimental results show that the proposed method performs well in protecting the security of private data and can effectively prevent the risk of tampering and leakage of private data.

Key words: vertical federated learning algorithm, privacy data, random forest regression model, composite chaos sequence, anti tampering encryption