Post-processing Private Synthetic Data for Improving Utility on Selected Measures

TL;DR

This paper presents a post-processing method that enhances the utility of private synthetic data for specific measures by resampling, while maintaining privacy guarantees, demonstrated through extensive experiments.

Contribution

It introduces a novel post-processing resampling technique that improves synthetic data utility for targeted measures without compromising privacy.

Findings

Consistently improves utility across multiple datasets

Effective with various synthetic data generation algorithms

Maintains strong privacy guarantees

Abstract

Existing private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.