DESI ONLINE SEMINAR
Abstract
The past decade has witnessed the fast development of machine learning techniques, and the key factor that drives the current progress is the unprecedented large-scale data. On the one hand, machine learning and big data can help improve various domains of people's life quality. On the other hand, they can also cause severe risks to people's privacy. In this talk, I will present our research on the intersection of data privacy and machine learning. First, I will show how to use machine learning techniques to assess and mitigate privacy risks for various types of data, including social network data, location data, and biomedical data. Then, I will present our research on quantifying privacy risks caused by machine learning models. In particular, I will discuss our newest results on membership inference and data reconstruction, as well as link stealing attacks against graph neural nets.
Bio
Yang Zhang is a faculty member at CISPA Helmholtz Center for Information Security, Germany. Previously, he was a group leader at CISPA. He obtained his Ph.D. degree from University of Luxembourg in November 2016 under the supervision of Prof. Sjouke Mauw and Dr. Jun Pang. Yang's research interests lie at the intersection of privacy and machine learning. Over the years, he has published multiple papers at top venues in computer science, including WWW, CCS, NDSS, USENIX Security, and IJCAI. His work has received NDSS 2019 distinguished paper award. Yang has served in the technical program committee of ACM CCS 2020 2019, ISMB 2019, WWW 2020, AAAI 2021, RAID 2020, ICWSM 2020, and PETS 2021 2020.
Join Zoom Meeting
https://unil.zoom.us/j/91927791048
Meeting ID: 919 2779 1048