Privacy-preserving data release leveraging optimal transport and particle gradient descent

We present a novel approach for differentially private data synthesis of protected tabular datasets, a relevant task in highly sensitive domains such as healthcare and government. Current state-of-the-art methods predominantly use marginal-based approaches, where a dataset is generated from private estimates of the marginals. In this paper, we introduce PrivPGD, a new generation method for marginal-based private data synthesis, leveraging tools from optimal transport and particle gradient descent. Our algorithm outperforms existing methods on a large range of datasets while …

art cs.cr cs.lg current data dataset datasets domains generated government healthcare novel privacy private private data release relevant sensitive state task transport