Privacy Preserving ML with Fully Homomorphic Encryption

A Google TechTalk, presented by Jordan Frery, 2024-05-08
ABSTRACT: In the rapidly evolving field of artificial intelligence, the commitment to data privacy and intellectual property protection during Machine Learning operations has become a foundational necessity for society and businesses handling sensitive data. This is especially critical in sectors such as healthcare and finance, where ensuring confidentiality and safeguarding proprietary information are not just ethical imperatives but essential business requirements.

This presentation goes into the role of Fully Homomorphic Encryption (FHE), based on the open-source library Concrete ML, in advancing secure and privacy-preserving ML applications.

We begin with an overview of Concrete ML, emphasizing how practical FHE for ML was made possible. This sets the stage for discussing how FHE is applied to ML inference, demonstrating its capability to perform secure inference on encrypted data across various models. After inference, we speak about another important FHE application, the FHE training and how encrypted data from multiple sources can be used for training without compromising individual user's privacy.

FHE has lots of synergies with other technologies, in particular Federated Learning: we show how this integration strengthens privacy-preserving features of ML models during the full pipeline, training and inference.

Finally, we address the application of FHE in generative AI and the development of Hybrid FHE models (which are the subject of our RSA 2024 presentation). This approach represents a strategic balance between intellectual property protection, user privacy and computational performance, offering solutions to the challenges of securing one of the most important AI applications of our times.

SPEAKERS:
Jordan Frery, Concrete ML Tech Lead and Research at Zama
Benoit Chevallier-Mames, VP Cloud and ML at Zama

DATE:
May 8 2024