  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 $\alpha=\infty$.
