Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

A Google TechTalk, presented by Jordan Fréry, 2023-01-17 ABSTRACT: In today’s digital age, protecting privacy has become increasingly difficult. However, new developments such as Fully Homomorphic Encryption (FHE) provide a means of safeguarding sensitive client information. We are excited to present Concrete-ML, our open-source library that allows for the seamless conversion of Machine Learning (ML) models into their FHE counterparts. With our technology, clients can enjoy zero-trust interactions with service providers while also enabling the deployment of ML models on untrusted servers without compromising the privacy of user data. Jordan Fréry is a research scientist at Zama