Improved Differentially Private Analysis of Variance

By Marika Swanberg, Ira Globus-Harris, Iris Griffith, Anna M. Ritz, Adam Groce, Andrew Bray. 2019

25 citations recorded by the source corpus.

Abstract Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines. Analysis of variance (ANOVA) in particular is used to detect dependence between a categorical and a numerical variable. Here we show how one can carry out this hypothesis test under the restrictions of differential privacy. We show that the F -statistic, the optimal test statistic in the public setting, is no longer optimal in the private setting, and we develop a new test statistic F1 with much higher statistical power. We show how to rigorously compute a reference distribution for the F1 statistic and give an algorithm that outputs accurate p-values. We implement our test and experimentally optimize several parameters. We then compare our test to the only previous work on private ANOVA testing, using the same effect size as that work. We see an order of magnitude improvement, with our test requiring only 7% as much data to detect the effect.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

The structure of optimal private tests for simple hypotheses (2018, background): “One line of work [17, 21, 40, 44, 49, 71, 76] designs differentially private versions of popular test statistics for testing goodness-of-fit, closeness, and independence, as well as private ANOVA, focusing on the performance at small sample sizes.”
Differentially Private Nonparametric Hypothesis Testing (2019, methodology): “The best existing test applicable in the same use case is that of Swanberg et al. [31].”