Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

Explore privacy-preserving machine learning techniques using Fully Homomorphic Encryption (FHE) in this 43-minute Google TechTalk presented by Jordan Fréry. Discover how Concrete-ML, an open-source library, enables the seamless conversion of Machine Learning models into FHE counterparts, allowing for zero-trust interactions between clients and service providers. Learn about the potential of deploying ML models on untrusted servers without compromising user data privacy. Gain insights into the latest developments in protecting sensitive information in the digital age from Jordan Fréry, a research scientist at Zama.

Privacy-Preserving Machine Learning with Fully Homomorphic Encryption