Privacy protection method and system based on federated learning, and storage medium

Abstract

The invention discloses a privacy protection method and system based on federated learning, and a storage medium, and the method comprises the steps: carrying out the encryption of a global model through employing a parameter encryption algorithm, and obtaining a ciphertext model; training on the ciphertext model by using local data, decrypting the obtained ciphertext gradient information and noise item to obtain a parameter gradient, updating the global model by using the parameter gradient, and repeating the steps until the model converges or reaches a specified iteration number to obtain amodel parameter; encrypting the model parameters to obtain encrypted model parameters, and updating the global model by adopting the encrypted model parameters to obtain a global encrypted model; andperforming local training on the encrypted global model to realize privacy protection. According to the method, semi-trusted federated learning participants can be effectively prevented from acquiringreal parameters of the global model and an output result of the intermediate model, and meanwhile, the participants can acquire a real prediction result by utilizing the finally trained encryption model.

Description

technical field [0001] The invention relates to the field of data protection, in particular to a privacy protection method, storage medium and system based on federated learning. Background technique [0002] With the wide application and development of big data mining and deep learning, more and more privacy leaks and data abuse incidents broke out frequently, making it a worldwide trend to pay attention to data privacy and security. Especially in distributed machine learning, distributed participants are reluctant to provide their own local training data due to privacy concerns, thus forming the phenomenon of "data islands". In order to deal with the problem of data privacy protection, break the practical difficulties of data islands, and meet the urgent needs of data joint use, the concept of federated learning and industrial-level application solutions were proposed. Federated learning is essentially a distributed machine learning framework. Under this framework, each p...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a privacy protection method, storage medium and system based on federated learning to solve the problem that existing private data cannot be effectively protected.

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