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Volume 15 Issue 3

Model-based Differentially Private Data Synthesis and Statistical Inference in Multiple Synthetic Datasets

Fang Liu^(a),(*)

Transactions on Data Privacy 15:3 (2022) 141 - 175

Abstract, PDF

(a) Department of Applied and Computational Mathematics and Statistics University of Notre Dame, Notre Dame, IN 46556, U.S.A.

e-mail:fang.liu.131 @nd.edu

Abstract

We propose the approach of model-based differentially private synthesis (modips) in the Bayesian framework for releasing individual-level surrogate/synthetic datasets with privacy guarantees. The modips technique integrates the concept of differential privacy into model-based data synthesis. We introduce several variants for the modips approach and multiple procedures for obtaining privacy-preserving posterior samples, a key step in the implementation of modips. The uncertainty from the sanitization and synthetic process in modips can be accounted for by releasing multiple synthetic datasets. We propose an inferential combination rule across multiple sets to generate final valid inferences. We run several empirical studies to demonstrate the application of modips and examine the impacts of the number of synthetic sets and the privacy budget allocation schemes on statistical inference based on synthetic data.

^* Corresponding author.