Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent
Abstract
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.
Cite
Text
Donhauser et al. "Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent." International Conference on Machine Learning, 2024.Markdown
[Donhauser et al. "Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/donhauser2024icml-privacypreserving/)BibTeX
@inproceedings{donhauser2024icml-privacypreserving,
title = {{Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent}},
author = {Donhauser, Konstantin and Abad, Javier and Hulkund, Neha and Yang, Fanny},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {11453-11473},
volume = {235},
url = {https://mlanthology.org/icml/2024/donhauser2024icml-privacypreserving/}
}