Abstract
Salil Vadhan: Differential Privacy & Statistical Inference – A Theoretical CS Perspective
I will give a theoretical computer scientist’s perspective on the research challenges in developing differentially private algorithms for statistical inference, in particular highlighting ways in which these directions may differ from a typical theoretical computer science mindset (or at least the mindset that I had a few years ago, before learning anything about statistical inference). Many of these challenges are very interesting theoretically as well as being relevant for bringing differential privacy to practice. As illustrative examples, I will discuss the problems of differentially private hypothesis testing and of producing differentially private confidence intervals.
Based in part on joint work with Vishesh Karwa, and on joint work with Gaboardi, Lim, and Rogers, as well as works by many others.
Frauke Kreuter: Gaining Record Linkage Consent: A Summary of Experimental Findings
No abstract available.