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 FHE-based 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.