Fiducial Matching: Differentially Private Inference for Categorical Data

TL;DR

This paper introduces a fiducial matching method for differentially private inference on categorical data, enabling more accurate confidence intervals and tests despite added privacy noise.

Contribution

It presents a novel simulation-based fiducial approach to approximate the distribution of estimates under differential privacy for categorical data.

Findings

Method achieves valid coverage in simulations.

Performs well on real survey data.

Offers computationally efficient inference under DP.

Abstract

The task of statistical inference, which includes the building of confidence intervals and tests for parameters and effects of interest to a researcher, is still an open area of investigation in a differentially private (DP) setting. Indeed, in addition to the randomness due to data sampling, DP delivers another source of randomness consisting of the noise added to protect an individual's data from being disclosed to a potential attacker. As a result of this convolution of noises, in many cases it is too complicated to determine the stochastic behavior of the statistics and parameters resulting from a DP procedure. In this work, we contribute to this line of investigation by employing a simulation-based matching approach, solved through tools from the fiducial framework, which aims to replicate the data generation pipeline (including the DP step) and retrieve an approximate distribution of the estimates resulting from this pipeline. For this purpose, we focus on the analysis of categorical (nominal) data that is common in national surveys, for which sensitivity is naturally defined, and on additive privacy mechanisms. We prove the validity of the proposed approach in terms of coverage and highlight its good computational and statistical performance for different inferential tasks in simulated and applied data settings.