  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 $>2^{500}$). 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.
