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Activity Number: 538 - Emerging Topics in Private Data Analysis
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: IMS
Abstract #314112
Title: Differentially Private Mean and Covariance Estimation
Author(s): Gautam Kamath*
Companies: University of Waterloo
Keywords: Differential privacy; parameter estimation

Given samples from a distribution, can we estimate its mean and covariance? Absent privacy considerations, the empirical estimates generally suffice. However, under the constraint of differential privacy, the picture changes dramatically. I will discuss a number of new challenges that arise in this setting, and the solutions we propose to address them. Some qualitative differences (with respect to the non-private setting) that we investigate include a dependence on the range of the data, improved rates with stronger moment bounds, and novel estimation techniques for multivariate settings.

Based on joint works with Sourav Biswas, Yihe Dong, Jerry Li, Vikrant Singhal, and Jonathan Ullman.

Authors who are presenting talks have a * after their name.

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