Differentially Private Low-dimensional Synthetic Data from High-dimensional Datasets

Differentially private synthetic data provide a powerful mechanism to enable data analysis while protecting sensitive information about individuals. However, when the data lie in a high-dimensional space, the accuracy of the synthetic data suffers from the curse of dimensionality. In this paper, we propose a differentially private algorithm to generate low-dimensional synthetic data efficiently from a high-dimensional dataset with a utility guarantee with respect to the Wasserstein distance. A key step of our algorithm is a private principal component analysis …

accuracy algorithm analysis cs.cr cs.ds cs.lg data data analysis datasets dimensionality generate information low math.pr math.st paper space stat.th synthetic synthetic data the curse of dimensionality