Privacy-preserving data release leveraging optimal transport and particle gradient descent
CoRR(2024)
摘要
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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