When: Friday, March 6, 3:00 PM
Where: Tyler 055

Abstract
Privacy-preserving data analysis has become a central challenge in modern statistics, with Differential Privacy (DP) emerging as the gold standard for protecting individual-level information. In this talk, I will present two projects at the intersection of DP and statistics. First, focusing on privatized inference, I will introduce a general framework for privacy-preserving statistical inference that constructs privatized interval estimators via consistent, privatized posterior quantile estimation. I theoretically establish mean-squared error consistency for the proposed estimators and demonstrate improved privacy-utility tradeoffs through extensive empirical experiments. Second, focusing on the inherent privacy guarantees provided by posterior sampling, I develop a unified Rényi divergence framework to quantify the DP guarantees achieved “for free” when releasing a single posterior sample. These theoretical results substantially tighten existing conservative bounds and broaden the class of Bayesian models for which inherent DP guarantees can be rigorously characterized.

Bio
Ruyu Zhou is a Ph.D. candidate in Statistics at the University of Notre Dame, advised by Dr. Fang Liu. Her research interests include trustworthy statistical learning, optimal transport, and applications in epidemiology and biostatistics. She is a recipient of the Notre Dame Scientific Artificial Intelligence Graduate Fellowship.