物联网中多密钥同态加密的联邦学习隐私保护方法OA北大核心CSTPCD

With federated learning,multiple distributed IoT devices can jointly train a global model by updating the transmission model without leaking raw data.However,federated learning systems are susceptible to model inference attacks,resulting in compromised system robustness and data privacy.A federated learning privacy protection method for multi-key homomorphic encryption in the Internet of Things is proposed to address the issues of existing federated learning solutions being unable to protect the confidentiality of shared gradients and resisting collusion attacks initiated by clients and servers.This method utilizes multi-key homomorphic encryption to achieve gradient update confidentiality protection.Firstly,by using proxy re-encryption technology,the ciphertext under different public keys is converted into encrypted data under the public key,ensuring that the cloud server can decrypt the gradient ciphertext.Then,IoT devices use their own public key and random secret factor to encrypt local gradient data,which can resist collusion attacks initiated by malicious devices and servers.Secondly,an identity authentication method based on hybrid cryptography was designed to achieve real-time verification of the identities of participants in federated modeling.In addition,in order to further reduce client computing costs,some decryption calculations are coordinated with trusted servers for computation,and users only need a small amount of computation.A comprehensive analysis was conducted on the proposed solution to evaluate its safety and efficiency.The results indicate that the proposed scheme meets the expected security requirements.Experimental simulation shows that compared to existing schemes,this scheme has lower computational overhead and can achieve faster and more accurate model training.