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OTHERS (INTERNATIONAL) Differentially Private Sampling from Distributions via Wasserstein Projection
Shokichi Takakura, Seng Pei Liew, Satoshi Hasegawa
arXiv.org (arXiv)
May 12, 2026
In this paper, we study the problem of sampling from a distribution under the constraint of differential privacy (DP).
Prior works measure the utility of DP sampling with density ratio-based measures such as KL divergence.
However, such formulations suffer from two key limitations: 1) they fail to capture the geometric structure of the support, and 2) they are not applicable when the supports of the distributions differ.
To deal with these issues, we develop a novel framework for DP sampling with Wasserstein distance as the utility measure.
In this formulation, we propose \emph{Wasserstein Projection Mechanism (WPM)}, a minimax optimal mechanism based on Wasserstein projection.
Furthermore, we develop efficient algorithms for computing the proposed mechanisms approximately and provide convergence guarantees.
Paper :
Differentially Private Sampling from Distributions via Wasserstein Projection
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