Preserving Target Distributions With Differentially Private Count Mechanisms

Computer Science > Cryptography and Security [Submitted on 1 Apr 2026] Preserving Target Distributions With Differentially Private Count Mechanisms Nitin Kohli, Paul Laskowski Differentially private mechanisms are increasingly used to publish tables of counts, where each entry represents the number of individuals belonging to a particular category. A distribution of counts summarizes the information in the count column, unlinking counts from categories. This object is useful for answering a class of research questions, but it is subject to statistical biases when counts are privatized with standard mechanisms. This motivates a novel design criterion we term accuracy of distribution. This study formalizes a two-stage framework for privatizing tables of counts that balances accuracy of distribution with two standard criteria of accuracy of counts and runtime. In the first stage, a distribution privatizer generates an estimate for the true distribution of counts. We introduce a new mechanism, called the cyclic Laplace, specifically tailored to distributions of counts, that outperforms existing general-purpose differentially private histogram mechanisms. In the second stage, a constructor algorithm generates a count mechanism, represented as a transition matrix, whose fixed-point is the privatized distribution of counts. We develop a mathematical theory that describes such transition matrices in terms of simple building blocks we call epsilon-scales. This theory informs the design of a new constructor algorithm that generates transition matrices with favorable properties more efficiently than standard optimization algorithms. We explore the practicality of our framework with a set of experiments, highlighting situations in which a fixed-point method provides a favorable tradeoff among performance criteria. Comments: 2026.2 PoPETS Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.01468 [cs.CR]   (or arXiv:2604.01468v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.01468 Focus to learn more Submission history From: Nitin Kohli [view email] [v1] Wed, 1 Apr 2026 23:25:05 UTC (1,082 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)