Differentially Private Multivariate Statistics with an Application to Contingency Table Analysis

TL;DR

This paper introduces improved Gaussian and Laplace mechanisms for differentially private multivariate statistics, especially contingency tables, demonstrating higher utility and power in hypothesis testing compared to existing methods.

Contribution

It develops rank-deficient James-Stein Gaussian mechanisms and minimal Laplace perturbation strategies under Gaussian differential privacy, enhancing utility in private multivariate data analysis.

Findings

Gaussian mechanisms outperform Laplace mechanisms at higher privacy levels.

Proposed mechanisms achieve higher statistical utility and test power.

Optimal Laplace calibration depends on more than global sensitivity.

Abstract

Differential privacy (DP) has become a rigorous central concept for privacy protection in the past decade. We use Gaussian differential privacy (GDP) in gauging the level of privacy protection for releasing statistical summaries from data. The GDP is a natural and easy-to-interpret differential privacy criterion based on the statistical hypothesis testing framework. The Gaussian mechanism is a natural and fundamental mechanism that can be used to perturb multivariate statistics to satisfy a $\mu$-GDP criterion, where $\mu>0$ stands for the level of privacy protection. Requiring a certain level of differential privacy inevitably leads to a loss of statistical utility. We improve ordinary Gaussian mechanisms by developing rank-deficient James-Stein Gaussian mechanisms for releasing private multivariate statistics, and show that the proposed mechanisms have higher statistical utilities. Laplace mechanisms, the most commonly used mechanisms in the pure DP framework, are also investigated under the GDP criterion. We show that optimal calibration of multivariate Laplace mechanisms requires more information on the statistic than just the global sensitivity, and derive the minimal amount of Laplace perturbation for releasing $\mu$-GDP contingency tables. Gaussian mechanisms are shown to have higher statistical utilities than Laplace mechanisms, except for very low levels of privacy. The utility of proposed multivariate mechanisms is further demonstrated using differentially private hypotheses tests on contingency tables. Bootstrap-based goodness-of-fit and homogeneity tests, utilizing the proposed rank-deficient James--Stein mechanisms, exhibit higher powers than natural competitors.