Federated learning privacy protection method based on homomorphic encryption in Internet of Vehicles

Abstract

The invention provides a federated learning privacy protection method based on homomorphic encryption in the Internet of Vehicles, which introduces federated learning based on homomorphic encryption into the Internet of Vehicles, improves a Paillier algorithm with addition homomorphic lines and an RSA algorithm with multiplication homomorphism, combines an AES algorithm and a step size confusion mode, and adopts a hierarchical encryption technology at the same time. According to the method, the addition homomorphism is completed at the edge end, and the multiplication homomorphism is completedat the cloud end to improve the encryption efficiency, so that federated learning malicious attacks are effectively prevented, and the delay caused by encryption is effectively reduced. The method can be applied to privacy protection in the Internet of Vehicles to introduce federated learning into the IoV so as to solve the problem of user privacy leakage. In order to further enhance the data safety, efficient homomorphic encryption is introduced into federated learning; moreover, a Paillier algorithm with addition homogeneity and an RSA algorithm with multiplication homogeneity are improved,and a federated learning architecture with full homomorphic encryption is constructed in combination with an AES algorithm and a step length confusion mode.

Description

technical field [0001] The invention relates to the field of privacy protection in the Internet of Vehicles, in particular to a privacy protection method for federated learning based on homomorphic encryption in the Internet of Vehicles. Background technique [0002] The massive data in the Internet of Vehicles enables it to have powerful processing and analysis capabilities, so as to realize intelligent traffic management, intelligent dynamic information services and intelligent vehicle control. Real-time traffic analysis is an extremely important part of the vehicle network, which is based on the collection of extensive traffic data and travel data. At present, most of the real-time traffic information of most IoV manufacturers comes from UGC (User Generated Content) data uploaded by users. However, traditional distributed computing is challenged by geographically dispersed data sharers. Secondly, in a shared business environment, users' travel privacy data is also facin...

AI Technical Summary

Problems solved by technology

First, federated learning is vulnerable to some cyber attacks, such as generative adversarial network (GAN) attacks

Second, the process of data encryption will also cause a huge delay in communication, etc.

Images