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arXiv · 2504.14696

Reveal-or-Obscure: A Differentially Private Sampling Algorithm for Discrete Distributions

Naima Tasnim·Atefeh Gilani·Lalitha Sankar·Oliver Kosut
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Abstract

We introduce a differentially private (DP) algorithm called reveal-or-obscure (ROO) to generate a single representative sample from a dataset of $n$ observations drawn i.i.d. from an unknown discrete distribution $P$. Unlike methods that add explicit noise to the estimated empirical distribution, ROO achieves $\epsilon$-differential privacy by randomly choosing whether to "reveal" or "obscure" the empirical distribution. While ROO is structurally identical to Algorithm 1 proposed by Cheu and Nayak (arXiv:2412.10512), we prove a strictly better bound on the sampling complexity than that established in Theorem 12 of (arXiv:2412.10512). To further improve the privacy-utility trade-off, we propose a novel generalized sampling algorithm called Data-Specific ROO (DS-ROO), where the probability of obscuring the empirical distribution of the dataset is chosen adaptively. We prove that DS-ROO satisfies $\epsilon$-DP, and provide empirical evidence that DS-ROO can achieve better utility under the same privacy budget of vanilla ROO.

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Naima Tasnim, Atefeh Gilani, Lalitha Sankar, Oliver Kosut. 2025-04-20. Reveal-or-Obscure: A Differentially Private Sampling Algorithm for Discrete Distributions. https://arxiv.org/abs/2504.14696

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