个人简介：Weijie Su is an Associate Professor in the Wharton Statistics and Data Science Department and, by courtesy, in the Department of Computer and Information Science, at the University of Pennsylvania. He is a co-director of Penn Research in Machine Learning. Prior to joining Penn, he received his Ph.D. in statistics from Stanford University under the supervision of Emmanuel Candes in 2016 and his bachelor’s degree from Peking University in 2011. His research interests span privacy-preserving data analysis, deep learning theory, optimization, mechanism design, and high-dimensional statistics. He serves as an associate editor of the Journal of the American Statistical Association (Theory and Methods) starting in 2023. He is a recipient of the Stanford Theodore Anderson Dissertation Award in 2016, an NSF CAREER Award in 2019, an Alfred Sloan Research Fellowship in 2020, the SIAM Early Career Prize in Data Science in 2022, and the IMS Peter Gavin Hall Prize in 2022.

摘要：Privacy-preserving data analysis has been put on a firm mathematical foundation since the introduction of differential privacy (DP) in 2006. This privacy definition, however, has some well-known weaknesses: notably, it does not tightly handle composition. In this talk, we propose a relaxation of DP that we term "f-DP", which has a number of appealing properties and avoids some of the difficulties associated with prior relaxations. This relaxation allows for lossless reasoning about composition and post-processing, and notably, a direct way to analyze privacy amplification by subsampling. We define a canonical single-parameter family of definitions within our class that is termed "Gaussian Differential Privacy", based on hypothesis testing of two shifted normal distributions. We prove that this family is focal to f-DP by introducing a central limit theorem, which shows that the privacy guarantees of any hypothesis-testing based definition of privacy converge to Gaussian differential privacy in the limit under composition. From a non-asymptotic standpoint, we introduce the Edgeworth Accountant, an analytical approach to compose $f$-DP guarantees of private algorithms. Finally, we demonstrate the use of the tools we develop by giving an impro