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 being highly scalable and offering the flexibility to incorporate additional domain-specific constraints.
@article{arxiv.2401.17823,
title = {Privacy-preserving data release leveraging optimal transport and particle gradient descent},
author = {Konstantin Donhauser and Javier Abad and Neha Hulkund and Fanny Yang},
journal= {arXiv preprint arXiv:2401.17823},
year = {2024}
}
Comments
Published at the Forty-first International Conference on Machine Learning