ICML 2024poster7 citations
Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent
Konstantin Donhauser, Javier Abad, Neha Hulkund, Fanny Yang
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.
BibTeX
@inproceedings{
donhauser2024privacypreserving,
title={Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent},
author={Konstantin Donhauser and Javier Abad and Neha Hulkund and Fanny Yang},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=4zN9tvZfns}
}