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.
