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[2504.14696] Reveal-or-Obscure: A Differentially Private Sampling Algorithm for Discrete Distributions
Summary
The paper presents a differentially private sampling algorithm, Reveal-or-Obscure (ROO), for generating samples from discrete distributions, improving upon existing methods with better sampling complexity and introducing a data-specific variant (DS-ROO).
Why It Matters
This research is significant as it addresses the challenge of maintaining privacy while sampling from discrete distributions, a critical issue in data science and machine learning. The proposed algorithms enhance the privacy-utility trade-off, making them valuable for applications requiring data confidentiality.
Key Takeaways
- ROO achieves ε-differential privacy by randomly revealing or obscuring data.
- The algorithm provides a better bound on sampling complexity than previous methods.
- Data-Specific ROO (DS-ROO) adapts the obscuring probability to improve utility.
- Empirical evidence supports DS-ROO's effectiveness under the same privacy budget.
- The research contributes to the ongoing discourse on privacy in machine learning.
Computer Science > Information Theory arXiv:2504.14696 (cs) [Submitted on 20 Apr 2025 (v1), last revised 17 Feb 2026 (this version, v3)] Title:Reveal-or-Obscure: A Differentially Private Sampling Algorithm for Discrete Distributions Authors:Naima Tasnim, Atefeh Gilani, Lalitha Sankar, Oliver Kosut View a PDF of the paper titled Reveal-or-Obscure: A Differentially Private Sampling Algorithm for Discrete Distributions, by Naima Tasnim and 2 other authors View PDF HTML (experimental) 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 ...
