As a popular machine learning method, federated learning (FL) can effectively solve the issues of data silos and data privacy. However, traditional federated learning schemes cannot provide sufficient privacy protection. Furthermore, most secure federated learning schemes based on local differential privacy (LDP) ignore an important issue: they do not consider each client’s differentiated privacy requirements. This paper introduces a perturbation algorithm (PDPM) that satisfies personalized local differential privacy (PLDP), resolving the issue of inadequate or excessive privacy protection for some participants due to the same privacy budget set for all clients. The algorithm enables clients to adjust the privacy parameters according to the sensitivity of their data, thus allowing the scheme to provide personalized privacy protection. To ensure the privacy of the scheme, we have conducted a strict privacy proof and simulated the scheme on both synthetic and real data sets. Experiments have demonstrated that our scheme is successful in producing high-quality models and fulfilling the demands of personalized privacy protection.

Federated learning is a distributed learning helpful approach for resolving data privacy concerns and eliminating data silos. Homomorphic encryption is a vital technology for preserving user privacy in federated learning, and current studies are mainly concentrated on a single-key environment. However, if one user key is exposed in a single-key environment, it implies that the whole system key has been revealed. To strengthen security, we should allow different participants of federated learning to choose different keys to encrypt their local models. The cloud server should finish model aggregation calculation on ciphertexts under different public keys. Besides, research in this area is insufficient to guarantee mobile users’ data integrity verification and authentication in open channels. Therefore, this article proposes a privacy protection federated learning scheme VPFL based on the BCP cryptosystem, which can verify user identity and data integrity in a multikey environment. First, this scheme employs the BCP cryptosystem with double trapdoors for data encryption and transmission, enhancing the user’s privacy security. Second, a method for verifying user data integrity and identity was created utilizing bilinear aggregate signatures and verifiable secret sharing. It can effectively eliminate some incorrect data of some users. Third, VPFL tolerates users dropping out during training while still guaranteeing high accuracy. Finally, theoretical analysis and experimental evaluation indicate that the proposed scheme is efficient and secure.

Federated learning is a distributed machine learning framework, which allows users to save data locally for training without sharing data. Users send the trained local model to the server for aggregation. However, untrusted servers may infer users’ private information from the provided data and mistakenly execute aggregation protocols to forge aggregation results. In order to ensure the reliability of the federated learning scheme, we must protect the privacy of users’ information and ensure the integrity of the aggregation results. This paper proposes an effective secure aggregation verifiable federated learning scheme, which has both high communication efficiency and privacy protection function. The scheme encrypts the gradients with a single mask technology to securely aggregate gradients, thus ensuring that malicious servers cannot deduce users’ private information from the provided data. Then the masked gradients are hashed to verify the aggregation results. The experimental results show that our protocol is more suited for bandwidth-constraint and offline-users scenarios.