Privacy-preserving data release leveraging optimal transport and particle gradient descent
This addresses privacy concerns in domains like healthcare and government, offering an incremental improvement over current marginal-based methods.
The paper tackles the problem of differentially private data synthesis for sensitive tabular datasets by introducing PrivPGD, a method that outperforms existing approaches on a wide range of datasets while being scalable and flexible.
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.