Privacy Under Hard Distortion Constraints

Abstract

We study the problem of data disclosure with privacy guarantees, wherein the utility of the disclosed data is ensured via a \emph{hard distortion} constraint. Unlike average distortion, hard distortion provides a deterministic guarantee of fidelity. For the privacy measure, we use a tunable information leakage measure, namely \textit{maximal $\alpha$-leakage} ($\alpha \in [1,\infty ]$), and formulate the privacy-utility tradeoff problem. The resulting solution highlights that under a hard distortion constraint, the nature of the solution remains unchanged for both local and non-local privacy requirements. More precisely, we show that both the optimal mechanism and the optimal tradeoff are invariant for any $\alpha >1$; i.e., the tunable leakage measure only behaves as either of the two extrema, i.e., mutual information for $\alpha =1$ and maximal leakage for $\alpha =\infty$.

5 pages, 1 figure

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