DPCopula is a differentially private data synthesization technique using Copula functions for multi-dimensional data. The core of the method is to compute a differentially private copula function to sample synthetic data. Copula functions are used to describe the dependence between multivariate random vectors and to build the multivariate joint distribution using one-dimensional marginal distributions. The parameters of the copula functions are estimated with differential privacy via maximum likelihood estimation and Kendall’s τ estimation. Experiments using both real datasets and synthetic datasets demonstrate that DPCopula generates highly accurate synthetic multidimensional data with significantly better utility than stateof-the-art techniques.

Reference

Haoran Li, Li Xiong, Xiaoqian Jiang. Differentially Private Synthesization of Multi-Dimensional Data using Copula Functions. In EDBT 2014 http://www.mathcs.emory.edu/aims/pub/li14edbt.pdf