MLCRDSITLGApr 30, 2020

A Primer on Private Statistics

arXiv:2005.00010v156 citations
Originality Synthesis-oriented
AI Analysis

This is an incremental contribution that synthesizes existing research for practitioners and researchers in privacy-preserving statistics.

The paper examines the relationship between two approaches in differentially private statistical estimation—empirical and population statistics—and demonstrates that methods developed for one can often be applied to the other, while also providing a comprehensive review of recent work in the field.

Differentially private statistical estimation has seen a flurry of developments over the last several years. Study has been divided into two schools of thought, focusing on empirical statistics versus population statistics. We suggest that these two lines of work are more similar than different by giving examples of methods that were initially framed for empirical statistics, but can be applied just as well to population statistics. We also provide a thorough coverage of recent work in this area.

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