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

• URL: http://arxiv.org/abs/2401.17823v3
• Date: Mon, 29 Jul 2024 14:12:50 GMT
• Title: Privacy-preserving data release leveraging optimal transport and particle gradient descent
• Authors: Konstantin Donhauser, Javier Abad, Neha Hulkund, Fanny Yang,
• Abstract summary: We introduce PrivPGD, a new generation method for 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.
• Score: 10.499611180329804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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