||2019 年 11 月 19 日（周二）上午 10:30-11:30
||复旦大学张江校区软件楼 102 第二会议室
The development of information and communication technologies have resulted in an unprecedented large-scale data being available. On the one hand, the large-scale data can help improve various domains of people's life quality and serve as the fuel for the development of machine learning techniques. On the other hand, it can cause severe risks to people's privacy. In this talk, I will first present our recent research results on using machine learning techniques to assess privacy risks of social network data. Then, I will focus on understanding privacy leakage caused by machine learning models. In particular, I will discuss our newest results on membership inference and data reconstruction.
Yang Zhang is an independent research group leader at CISPA Helmholtz Center for Information Security, Saarbruecken, Germany. Previously, he was a postdoc working in the group of Prof. Michael Backes at CISPA from January 2017 to December 2018. He obtained his Ph.D. degree from University of Luxembourg in November 2016 under the supervision of Prof. Sjouke Mauw and Dr. Jun Pang. Prior to that, he obtained his bachelor (2009) and master (2012) degrees from Shandong University. His research mainly concentrates on data privacy. Topics include machine learning privacy, biomedical privacy, social network privacy, and location privacy. Besides, he also works on urban computing, social media analysis, and data mining. Yang 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 2019, ISMB 2019, WWW 2020, and PETS 2020.