Gaussian Differential Privacy (GDP)


Data privacy has been put on firm theoretical foundations since the birth of differential privacy. The fruitful decade has witnessed many theoretical and practical successes, including the remarkable deployment in Chrome and iOS. However, a major obstacle to further applications is the extra sophistication in various theorems in differential privacy, most notably composition and subsampling. Precise characterization of these properties is of vital importance in practice. We propose a hypothesis testing based framework called f-DP, which includes Gaussian differential privacy (GDP) as the most useful special case. All desired properties admit elegant and tight statements in this framework. In addition, a central limit theorem is proved and shows the universality of GDP. As a comprehensive application, we show how the tools we develop improves the analysis of the privacy of perturbed stochastic gradient descent.

Based on joint work with Aaron Roth and Weijie Su.


February 26th, 2019

15:00 ~ 16:30


Jinshuo Dong, PhD at University of Pennsylvania


Room 102, School of Information Management & Engineering, Shanghai University of Finance & Economics

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