Reza Shokri - Trusting Machine Learning: Privacy, Robustness, and Interpretability Challenges

10:00
Lundi
8
Jui
2019
  • Séminaire
Lieu : 
Organisé par : 
O. Goga et C. Castelluccia
Intervenant : 
Reza Shokri (National University of Singapore)
Équipes : 

 

Reza shokri is an Assistant Professor of Computer Science at the National University of Singapore (NUS), where he holds the NUS Presidential Young Professorship. His research is on adversarial and privacy-preserving computation, notably for machine learning algorithms. He is an active member of the security and privacy community, and has served as a PC member of IEEE S&P, ACM CCS, Usenix Security, NDSS, and PETS. He received the Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies in 2018, for his work on analyzing the privacy risks of machine learning models, and was a runner-up in 2012, for his work on quantifying location privacy. He obtained his PhD from EPFL.

Machine learning algorithms have shown an unprecedented predictive power for many complex learning tasks. As they are increasingly being deployed in large scale critical applications for processing various types of data, new questions related to their trustworthiness would arise. Can machine learning algorithms be trusted to have access to individuals' sensitive data? Can they be robust against noisy or adversarially perturbed data? Can we reliably interpret their learning process, and explain their predictions? In this talk, I will go over the challenges of building trustworthy machine learning algorithms in centralized and distributed (federated) settings, and will discuss the inter-relation between privacy, robustness, and interpretability.