Differentially Private Data Release via Statistical Election to Partition Sequentially

TL;DR

This paper introduces STEPS, a new differentially private data synthesis method that prioritizes important attributes for better utility preservation, demonstrated on survey data and outperforming existing methods.

Contribution

STEPS is a novel DIPS approach that sequentially partitions data based on attribute importance, improving utility while maintaining differential privacy.

Findings

STEPS better preserves population-level information.

STEPS outperforms PrivBayes and other methods in utility.

Effective privacy budget management in the proposed algorithm.

Abstract

Differential Privacy (DP) formalizes privacy in mathematical terms and provides a robust concept for privacy protection. DIfferentially Private Data Synthesis (DIPS) techniques produce and release synthetic individual-level data in the DP framework. One key challenge to developing DIPS methods is preservation of the statistical utility of synthetic data, especially in high-dimensional settings. We propose a new DIPS approach, STatistical Election to Partition Sequentially (STEPS) that partitions data by attributes according to their importance ranks according to either a practical or statistical importance measure. STEPS aims to achieve better original information preservation for the attributes with higher importance ranks and produce thus more useful synthetic data overall. We present an algorithm to implement the STEPS procedure and employ the privacy budget composability to ensure the overall privacy cost is controlled at the pre-specified value. We apply the STEPS procedure to both simulated data and the 2000-2012 Current Population Survey youth voter data. The results suggest STEPS can better preserve the population-level information and the original information for some analyses compared to PrivBayes, a modified Uniform histogram approach, and the flat Laplace sanitizer.