Research on privacy preservation of member inference attacks in online inference process for vertical federated learning linear model

Abstract

With the development of big data and the introduction of data security regulations, the awareness of privacy protection has gradually increased, and the phenomenon of data isolation has become more and more serious.Federated learning technology as one of the effective methods to solve this problem has become a hot spot of concern.In the online inference process of vertical federated learning, the current mainstream methods do not consider the protection of data identity, which is easy to leak user privacy.A privacy protection method for member inference attacks was proposed in the online inference process of the vertical federated linear model.A filter with a false positive rate was constructed to avoid the accurate positioning of data identity to ensure the security of data.Homomorphic encryption was used to realize the full encrypted state of the online inference process and protect the intermediate calculation results.According to the ciphertext multiplication property of homomorphic encryption, the random number multiplication method was used to mask data, which ensured the security of the final inference result.This scheme further improved the security of user privacy in the online inference process of vertical federated learning and had lower computation overhead and communication costs.

Keywords

• federated learning;vertical federated learning linear model;online inference;partial homomorphic encryption;data masking