Tutorial and Practices on Hybrid Privacy Enhancing Technologies

• Date/Time: Zurich, Switzerland on May 26-30, 2024
• Format: Tutorial with practical sessions

Secure multiparty computation (MPC), fully homomorphic encryption (FHE), federated learning (FL), trusted execution environments (TEEs) and differential privacy (DP) are prominent examples of emerging PETs. They enable the computation of a function without revealing the input data. By incorporating these techniques, organizations strike a balance between preserving the privacy of sensitive input data and deriving valuable insights from data analysis, optimizing the privacy-utility tradeoff.

This tutorial will walk you through these technologies and show how to combine them in order to implement a hybrid PETs use-case. In the practical session you will learn how to implement a complete and hybrid privacy-preserving machine learning workflow using different PETs tools and libraries for PSI, MPC, FHE amongst others.

Tentative Program:

Mariya Georgieva

Mariya Georgieva leads Inpher’s cryptography research and manages a team of engineers. She is responsible for developing "Secret Computing'' technology and for proposing, designing, and developing privacy preserving solutions, including MPC, FHE, and FL. Dr. Georgieva is co-author of the open-source TFHE library.

Sergiu Carpov

Sergiu Carpov is a senior cryptographer at Inpher and his main research interests include efficient secure computation protocols and compilation techniques for privacy-preserving applications.