Machine Learning Training on Encrypted Data with TFHE
Luis Montero, Jordan Fréry, Celia Kherfallah, Roman Bredehoft, Andrei Stoian · 2024
We present an approach for outsourcing the training of machine learning (ML) models while preserving data confidentiality from malicious parties. We use fully homomorphic encryption (FHE) to build a unified training framework that works on encrypted data and learns quantized ML models. Our approach finds future applications in collaborative settings involving multiple parties working on confidential data, which can be horizontally or vertically split between data owners. We train logistic regression and multi-layer perceptrons on several datasets and show results that are comparable to the state-of-the-art.