In this chapter we presented the experimental evaluation of the {\thething} privacy schemes and the dummy {\thething} selection module, that we developed in Chapter~\ref{ch:lmdk-prv}, on real and synthetic data sets.
The \texttt{Adaptive} scheme is the most reliable and best performing scheme, in terms of overall data utility, with minimal tuning across most of the cases.
\texttt{Skip} performs optimally in data sets with a smaller target value range, where approximation fits best.
The dummy {\thething} selection module introduces a reasonable data utility decline to all of our schemes; however, the \texttt{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 temporal 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 \texttt{Adaptive} scheme provides better data utility than the user-level privacy protection.
