In this chapter we presented the experimental evaluation of the {\thething} privacy mechanisms and the privacy-preserving {\thething} selection mechanism that we developed in Chapter~\ref{ch:lmdk-prv}, on real and synthetic data sets.
Skip performs optimally in data sets with a smaller target 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.\kat{it would be nice to see it clearly on Figure 5.5. (eg, by including another bar that shows adaptive without landmark selection)}
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.
Finally, the contribution of the {\thething} privacy on enhancing the data quality, while preserving $\epsilon$-differential privacy is demonstrated by the fact that the selected, Adaptive mechanism provides better data quality than the user-level mechanism.
