A Tight Context-aware Privacy Bound for Histogram Publication

• URL: http://arxiv.org/abs/2508.18832v1
• Date: Tue, 26 Aug 2025 09:12:23 GMT
• Title: A Tight Context-aware Privacy Bound for Histogram Publication
• Authors: Sara Saeidian, Ata Yavuzyılmaz, Leonhard Grosse, Georg Schuppe, Tobias J. Oechtering,
• Abstract summary: We analyze the privacy guarantees of the Laplace mechanism releasing the histogram of a dataset through the lens of pointwise maximal leakage (PML)<n>We show that when the probability of each histogram bin is bounded away from zero, stronger privacy protection can be achieved for a fixed level of noise.
• Score: 15.762008564991207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We analyze the privacy guarantees of the Laplace mechanism releasing the histogram of a dataset through the lens of pointwise maximal leakage (PML). While differential privacy is commonly used to quantify the privacy loss, it is a context-free definition that does not depend on the data distribution. In contrast, PML enables a more refined analysis by incorporating assumptions about the data distribution. We show that when the probability of each histogram bin is bounded away from zero, stronger privacy protection can be achieved for a fixed level of noise. Our results demonstrate the advantage of context-aware privacy measures and show that incorporating assumptions about the data can improve privacy-utility tradeoffs.

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