Post-Quantum Privacy-Preserving Federated Learning via Anti-Gradients Leakage Based on Secure Multi-party Computation Techniques

Abstract

As a privacy-preserving technique, Secure Multi-Party Computation (SMPC) has been extensively applied in Neural Networks (NN) privacy-preserving schemes, including secret sharing, homomorphic encryption, and zero-knowledge proof. Federated Learning (FL) is applied to train models from scattered data, which can protect the client's data. Therefore, the leakage of gradient in Federated Learning has been a risk of privacy and security. We construct a post-quantum privacy-preserving cryptography algorithm, in which gradient can be protected by SMPC techniques. Non-interactive zero-knowledge proof, Shamir secret sharing, and homomorphic encryption techniques are applied to guarantee the security of gradient transmission. L WE-based Kyber key exchange protocol is also applied to be resistant to quantum attack.