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+\section{Evaluation}
+\label{sec:eval-sum}
+
+In this chapter we presented the experimental evaluation of the {\thething} privacy mechanisms and privacy-preserving {\thething} selection mechanism, that we developed in chapter~\ref{ch:lmdk-prv}, on real and synthetic data sets.
+The Adaptive mechanism is the most reliable and best performing mechanism, in terms of overall data utility, with minimal tuning across most cases.
+Skip performs optimally in data sets with a lower value range where approximation fits best.
+The {\thething} selection component introduces a reasonable data utility decline to all of our mechanisms however, the Adaptive handles it well and bounds the data utility to higher levels compared to user-level protection.
+In terms of temporal correlation, we observe that under moderate and strong temporal correlation, a greater average regular--{\thething} event distance in a {\thething} distribution causes greater overall privacy loss.