Comparative Study of Differentially Private Data Synthesis Methods

Abstract

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 (DS) is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional DS 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 DS 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.

The main paper is the first 48 pages (8 pages of reference). The rest of the pages (49 - 67) contain the Supplemental Material

References in corpus (6)

• Deep Learning with Differential Privacy
• RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
• Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12
• Generalized Gaussian Mechanism for Differential Privacy
• Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
• Privacy-Preserving Data Analysis for the Federal Statistical Agencies

Cited by in corpus (12)

• Really Useful Synthetic Data -- A Framework to Evaluate the Quality of Differentially Private Synthetic Data
• Differential Privacy for Government Agencies -- Are We There Yet?
• Plausible Deniability for Privacy-Preserving Data Synthesis
• Kamino: Constraint-Aware Differentially Private Data Synthesis
• Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy
• Generating Poisson-Distributed Differentially Private Synthetic Data
• Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?
• Model-based Differentially Private Data Synthesis and Statistical Inference in Multiply Synthetic Differentially Private Data
• Differentially Private Data Generation with Missing Data
• SoK: Chasing Accuracy and Privacy, and Catching Both in Differentially Private Histogram Publication
• pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity
• Differentially Private Data Release via Statistical Election to Partition Sequentially