Faculty Candidate: Reza Shokri
480 Dreese Labs
2015 Neil Avenue
Columbus, Ohio 43210
Data Privacy: How to Survive Inference Avalanche
Underestimating the power of inference attacks is the major reason why data privacy mechanisms fail. In this talk, I will describe my general approach to quantifying privacy and illustrate its applications by showing how to rigorously measure privacy risks of location data and machine-learning models. I will then discuss my current research at the junction of privacy and data science in two important practical scenarios: generating privacy-preserving synthetic data and building accurate deep-learning models that respect privacy of the training data.
Bio: Reza Shokri is a postdoctoral researcher at Cornell Tech. His research focuses on quantitative analysis of privacy, as well as design and implementation of privacy technologies for practical applications. His work on quantifying location privacy was recognized as a runner-up for the Award for Outstanding Research in Privacy Enhancing Technologies (PET Award). Recently, he has focused on privacy-preserving generative models and privacy in machine learning. He received his PhD from EPFL.
Host: Steve Lai