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• PrivSyn - Differentially Private Data Synthesis

PrivSyn - Differentially Private Data Synthesis

• USENIX Security ‘21 - PrivSyn: Differentially Private Data Synthesis
• Zhikun Zhang, Zhejiang University and CISPA Helmholtz Center for Information Security; Tianhao Wang, Ninghui Li, and Jean Honorio, Purdue University; Michael Backes, CISPA Helmholtz Center for Information Security; Shibo He and Jiming Chen, Zhejiang University and Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies; Yang Zhang, CISPA Helmholtz Center for Information Security
• View the full USENIX Security ‘21 Program at https://www.usenix.org/conference/usenixsecurity21/technical-sessions
• USENIX Security ‘21 Full Proceedings (PDF, 346 MB)

Abstract

In differential privacy (DP), a challenging problem is to generate synthetic datasets that efficiently capture the useful information in the private data. The synthetic dataset enables any task to be done without privacy concern and modification to existing algorithms. In this paper, we present PrivSyn, the first automatic synthetic data generation method that can handle general tabular datasets (with 100 attributes and domain size greater than 2500). PrivSyn is composed of a new method to automatically and privately identify correlations in the data, and a novel method to generate sample data from a dense graphic model. We extensively evaluate different methods on multiple datasets to demonstrate the performance of our method.