A Latent Class Modeling Approach for Generating Synthetic Data and Making Posterior Inferences from Differentially Private Counts

TL;DR

This paper introduces a latent class modeling method to post-process differentially private counts, enabling the creation of synthetic data and posterior inference about confidential counts, demonstrated on real survey datasets.

Contribution

It proposes a novel latent class approach for post-processing differentially private counts to generate synthetic data and perform inference, improving coherence and utility.

Findings

Effective in creating coherent synthetic data from noisy counts

Enables posterior inference on confidential counts

Demonstrated on real survey datasets

Abstract

Several algorithms exist for creating differentially private counts from contingency tables, such as two-way or three-way marginal counts. The resulting noisy counts generally do not correspond to a coherent contingency table, so that some post-processing step is needed if one wants the released counts to correspond to a coherent contingency table. We present a latent class modeling approach for post-processing differentially private marginal counts that can be used (i) to create differentially private synthetic data from the set of marginal counts, and (ii) to enable posterior inferences about the confidential counts. We illustrate the approach using a subset of the 2016 American Community Survey Public Use Microdata Sets and the 2004 National Long Term Care Survey.