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Poster
CoinPress: Practical Private Mean and Covariance Estimation
Sourav Biswas · Yihe Dong · Gautam Kamath · Jonathan Ullman

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #254
in Poster Session 2 »

We present simple differentially private estimators for the parameters of multivariate sub-Gaussian data that are accurate at small sample sizes. We demonstrate the effectiveness of our algorithms both theoretically and empirically using synthetic and real-world datasets---showing that their asymptotic error rates match the state-of-the-art theoretical bounds, and that they concretely outperform all previous methods. Specifically, previous estimators either have weak empirical accuracy at small sample sizes, perform poorly for multivariate data, or require the user to provide strong a priori estimates for the parameters.

Author Information

Sourav Biswas (University of Waterloo)
Yihe Dong (Google)

Machine learning researcher and engineer interested in geometric deep learning, graph representation learning, and natural language processing.

Gautam Kamath (University of Waterloo)
Jonathan Ullman (Northeastern University)

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