FHAIM Puts Fully Homomorphic Encryption Into Synthetic Data Training

An arXiv paper introduces FHAIM, a framework that uses fully homomorphic encryption to train synthetic data generators on encrypted tabular data. The goal is to preserve confidentiality during training while still enabling privacy-preserving synthetic data generation.