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arXiv · 2401.17823

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

Konstantin Donhauser·Javier Abad·Neha Hulkund·Fanny Yang
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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.

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Konstantin Donhauser, Javier Abad, Neha Hulkund, Fanny Yang. 2024-01-31. Privacy-preserving data release leveraging optimal transport and particle gradient descent. https://arxiv.org/abs/2401.17823

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