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\section{Summary}
\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.
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.
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.
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.