Recent advances of privacy-preserving machine learning based on(Fully) Homomorphic Encryption

Fully Homomorphic Encryption(FHE),known for its ability to process encrypted data without decryption,is a promising technique for solving privacy concerns in the machine learning era.However,there are many kinds of available FHE schemes and way more FHEbased solutions in the literature,and they are still fast evolving,making it difficult to get a complete view.This article aims to introduce recent representative results of FHE-based privacy-preserving machine learning,helping users understand the pros and cons of different kinds of solutions,and choose an appropriate approach for their needs.