Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge

TL;DR

This paper evaluates and compares various differentially private synthetic data algorithms from a NIST challenge, providing insights into their accuracy, usability, and implications for policy and practice.

Contribution

It offers a comprehensive evaluation of multiple algorithms using real challenge data, introduces new utility metrics, and discusses their suitability for policy use.

Findings

Certain algorithms outperform others in data utility.

New utility metrics provide better assessment of synthetic data quality.

Recommendations for algorithm selection based on use-case.

Abstract

Differentially private synthetic data generation offers a recent solution to release analytically useful data while preserving the privacy of individuals in the data. In order to utilize these algorithms for public policy decisions, policymakers need an accurate understanding of these algorithms' comparative performance. Correspondingly, data practitioners require standard metrics for evaluating the analytic qualities of the synthetic data. In this paper, we present an in-depth evaluation of several differentially private synthetic data algorithms using actual differentially private synthetic data sets created by contestants in the 2018-2019 National Institute of Standards and Technology Public Safety Communications Research (NIST PSCR) Division's ``Differential Privacy Synthetic Data Challenge.'' We offer analyses of these algorithms based on both the accuracy of the data they created and their usability by potential data providers. We frame the methods used in the NIST PSCR data challenge within the broader differentially private synthetic data literature. We implement additional utility metrics, including two of our own, on the differentially private synthetic data and compare mechanism utility on three categories. Our comparative assessment of the differentially private data synthesis methods and the quality metrics shows the relative usefulness, the general strengths and weaknesses, and offers preferred choices of algorithms and metrics. Finally we describe the implications of our evaluation for policymakers seeking to implement differentially private synthetic data algorithms on future data products.