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.

翻译：我们提出了一种针对受保护表格数据集的差分隐私数据合成新方法，这在医疗和政府等高度敏感领域具有重要应用价值。当前主流方法主要采用基于边际的方法，即从边际分布的隐私估计中生成数据集。本文提出PrivPGD——一种基于边际的隐私数据合成新方法，该方法利用最优传输理论与粒子梯度下降工具。我们的算法在各类数据集上均优于现有方法，同时具备高度可扩展性，并能灵活融入特定领域的约束条件。