Privacy Preserving Machine Learning Workshop

Workshop Schedule

15 December - Workshop Session - Room B

9h00 - 10h00 - Andrei Stoian, Zama ML, Paris Keynote: PPML with TFHE

10h00 - 11h00 - Valerio Maggio, Anaconda Keynote: PPML, ML on data you cannot see

Keynote Speakers

slides

A keynote PPML with TFHE about Fully Homomorphic Encryption (FHE), delivered by Andrei Stoian (Data Scientist at Zama, Concrete-ML group);

A second keynote PPML: ML on data you cannot see on differential privacy and machine learning delivered by Valerio Maggio (Data Scientist and Researcher at Anaconda);

Workshop: Privacy-Preserving Machine Learning

Privacy-preserving machine learning is an increasingly vital field as organizations and individuals strive to leverage machine learning while safeguarding sensitive data. This workshop addresses the growing demand for privacy-enhancing techniques. It offers an up-to-date overview of the key concepts, methods, and tools. Participants will be introduced to essential cryptographic techniques.

The workshop's structure consists of a keynote on Fully Homomorphic Encryption (FHE) by Andrei Stoian, a Data Scientist at Zama's Concrete-ML group. Additionally, it features contributions from various experts, including PhD students, early-stage researchers, and industry professionals, focusing on topics such as Differential Privacy, Federated Learning, Fully Homomorphic Encryption, Secure Multi-Party Computation, and Trusted Execution Environment.

The keynote delves into the practical aspects of FHE, discussing both advancements and challenges. Furthermore, the workshop invites contributions related to these topics, providing a platform for discussing real-world applications, enabling young researchers to showcase their work, and receive valuable insights and suggestions.

Registration

To register follow instructions to register to the main conference.