In this chapter we presented the experimental evaluation of the {\thething} privacy schemes and the privacy-preserving {\thething} selection scheme that we developed in Chapter~\ref{ch:lmdk-prv}, on real and synthetic data sets.
The Adaptive scheme is the most reliable and best performing scheme, in terms of overall data utility, with minimal tuning across most of the cases.
The {\thething} selection module introduces a reasonable data utility decline to all of our schemes 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.
Finally, the contribution of the {\thething} privacy on enhancing the data utility, while preserving $\varepsilon$-differential privacy, is demonstrated by the fact that the selected Adaptive scheme provides better data utility than the user-level privacy protection.
