A composite privacy protection method, system, computer equipment and storage medium

Abstract

The present invention provides a compound privacy protection method, system, computer equipment and storage medium. The method includes: generating the encryption public key of the client and the decryption private key of the server through a trusted third party in advance according to the weight of the client; The server sends the first model and the first model parameters to the client; the client trains the first model according to local data, and updates the first model parameters to the second model parameters; Privacy algorithm, the client adds noise to the second model parameters to obtain third model parameters; according to the encrypted public key, the client performs functional encryption on the third model parameters to obtain an encrypted model and send it to the server; according to the decryption private key, the server decrypts the encrypted model to obtain a global model. The invention not only strengthens privacy protection, but also improves model service quality and learning efficiency.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a compound privacy protection method, system, computer equipment and storage medium oriented to federated learning. Background technique [0002] With the rapid development of artificial intelligence, the use of machine deep learning models for training and classification prediction is widely used. However, due to the limited data of each user, the accuracy of machine learning is reduced, and for the protection of data privacy and security, each The existence of problems such as the inability to directly exchange data between users limits the development of machine learning. Subsequently, a federated learning that allows full use of decentralized training equipment for model training without the need for original data came into being. . Although federated learning provides convenience for further breaking data islands and providing more accurate services,...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a compound privacy protection method oriented to federated learning, which overcomes the problem that the existing privacy protection method ignores the protection of the weight of the client, and achieves the effect of protecting the privacy of the client data source and preventing the threat of data-level privacy leakage At the same time, it overcomes the problem that the existing technology ignores the model service quality and learning efficiency of federated learning, and achieves the effect of preventing the threat of content-level privacy leakage and ensuring the model service quality and learning efficiency.

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