Advanced Privacy Protection in Federated Learning using Server-initiated Homomorphic Encryption
Abstract
Federated learning (FL) has been widely adopted to provide machine learning (ML) privacy, protecting sensitive user data from leakage. However, there are still attacks that could exploit FL to access users' sensitive data, such as model inversion attacks, property inference attacks, and membership inference attacks. Various solutions were proposed to secure FL using various privacy-preserving techniques, such as differential privacy, homomorphic encryption, and multi-party encryption. However, existing solutions often add noise to the model that hinders the accuracy, or introduce large computational overhead that makes them impractical to use. In this paper, we propose a new privacy protection scheme for FL that uses homomorphic encryption (HE), noise, and secret sharing to protect users' sensitive data from up to n-2 adversarial clients and the server colluding. The computational overhead is minimised by transferring expensive computations of HE to the server, requiring only the encryption and homomorphic addition to be carried out by clients. We provide proof sketches to validate the security of our scheme, and experimental results to demonstrate the practicality of our proposed scheme. The results show that our scheme adds only up to 8% overhead without losing any accuracy to base FL models, showing minimal overhead without losing accuracy, regardless of the data used.
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