research areaMethods and Algorithms, research areaPrivacyWorkshop at ICML
content type paperpublished July 2021

Non-parametric Differentially Private Confidence Intervals for the Median

In collaboration with Boston University, Harvard University, Institute for Employment Research, University of Maryland, University of Pennsylvania

AuthorsJoerg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, Adam Smith

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This paper was accepted at the Theory and Practice of Differential Privacy workshop at the ICML 2021 conference.

This paper was accepted at the Journal of Survey Statistics and Methodology Journal in 2022.

Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data. However, research on proper statistical inference, that is, research on properly quantifying the uncertainty of the (noisy) sample estimate regarding the true value in the population, is currently still limited. This paper proposes and evaluates several strategies to compute valid differentially private confidence intervals for the median. Instead of computing a differentially private point estimate and deriving its uncertainty, we directly estimate the interval bounds and discuss why this approach is superior if ensuring privacy is important. We also illustrate that addressing both sources of uncertainty--the error from sampling and the error from protecting the output--simultaneously should be preferred over simpler approaches that incorporate the uncertainty in a sequential fashion. We evaluate the performance of the different algorithms under various parameter settings in extensive simulation studies and demonstrate how the findings could be applied in practical settings using data from the 1940 Decennial Census.

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