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Title: PrivSyn: Differentially Private Data Synthesis
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 > 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.  more » « less
Award ID(s):
1931443
NSF-PAR ID:
10322940
Author(s) / Creator(s):
; ; ; ; ; ; ;
Editor(s):
Bailey, Michael; Greenstadt, Rachel
Date Published:
Journal Name:
Proceedings of the 30th USENIX Security Symposium
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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