Open Access
May 2020 Comparative Study of Differentially Private Data Synthesis Methods
Claire McKay Bowen, Fang Liu
Statist. Sci. 35(2): 280-307 (May 2020). DOI: 10.1214/19-STS742


When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional data synthesis techniques often rely on strong assumptions of a data intruder’s behaviors and background knowledge to assess disclosure risk. Differential privacy (DP) formulates a theoretical approach for a strong and robust privacy guarantee in data release without having to model intruders’ behaviors. Efforts have been made aiming to incorporate the DP concept in the data synthesis process. In this paper, we examine current DIfferentially Private Data Synthesis (DIPS) techniques for releasing individual-level surrogate data for the original data, compare the techniques conceptually and evaluate the statistical utility and inferential properties of the synthetic data via each DIPS technique through extensive simulation studies. Our work sheds light on the practical feasibility and utility of the various DIPS approaches, and suggests future research directions for DIPS.


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Claire McKay Bowen. Fang Liu. "Comparative Study of Differentially Private Data Synthesis Methods." Statist. Sci. 35 (2) 280 - 307, May 2020.


Published: May 2020
First available in Project Euclid: 3 June 2020

MathSciNet: MR4106606
Digital Object Identifier: 10.1214/19-STS742

Keywords: differential privacy , DIPS , nonparametric DIPS , parametric DIPS , Statistical disclosure limitation , sufficient statistics

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.35 • No. 2 • May 2020
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