CoinPress: Practical Private Mean and Covariance Estimation
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Publication:6342664
arXiv2006.06618MaRDI QIDQ6342664
Author name not available (Why is that?)
Publication date: 11 June 2020
Abstract: We present simple differentially private estimators for the mean and covariance 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.
Has companion code repository: https://github.com/twistedcubic/coin-press
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