演讲人： Ke Yi Hong Kong University
时间： 2024-10-22 15:00-2024-10-22 16:30
地点：FIT 1-315 or Tencent Meeting: 161-219-779
内容：

Because of its mathematical rigor and applicability to a broad class of problems, differential privacy (DP) has now become the de facto standard for protecting personal information, widely adopted by both governments and industry.  Most classical DP mechanisms are based on the sensitivity of the target function, but many basic functions, such as sum, mean, median, max/min, as well as most SQL queries, do not have a bounded sensitivity.  For such functions, existing algorithms often take an ad hoc approach when measuring their utility.  In this talk, I will present a unified and principled framework to define the optimality of DP mechanisms for such functions, based on a slightly relaxed notion of instance optimality, and show how this can be achieved for the aforementioned problems.

个人简介:

Ke Yi is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He obtained his Bachelor's degree from Tsinghua University and PhD from Duke University, both in computer science. His research interests include database theory and systems, data security and privacy, and theoretical computer science.  He has received two SIGMOD Best Paper Awards (2016, 2022), two SIGMOD Best Paper Honorable Mentions (2022, 2024), a SIGMOD Best Demonstration Award (2015), and a PODS Test-of-Time Award (2022).  He is the PC Chair of PODS 2026 and ICDT 2021, and serves on the editorial board of ACM Transactions on Database Systems.