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

Abstract:We present a novel approach for differentially private synthesis of protected tabular , a relevant task in highly sensitive such as . Current state-of-the- predominantly use marginal-based approaches, where a 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

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