Differentially Private Sampling from Distributions via Wasserstein Projection

ArXi:2605.10015v1 Announce Type: cross 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, and 2) they are not applicable when the s of the distributions differ. To deal with these issues, we develop a novel framework for DP sampling with Wasserstein distance as the utility measure.