A Tight Context-aware Privacy Bound for Histogram Publication

TL;DR

This paper introduces a refined privacy analysis for histogram publication using pointwise maximal leakage, showing that data distribution assumptions can enhance privacy guarantees for a fixed noise level.

Contribution

It provides a new context-aware privacy bound for the Laplace mechanism in histogram release, improving privacy-utility tradeoffs by leveraging data distribution assumptions.

Findings

Stronger privacy guarantees when histogram bin probabilities are bounded away from zero.

Context-aware analysis offers better privacy bounds than traditional differential privacy.

Incorporating data distribution assumptions enhances privacy-utility tradeoffs.

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.