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evaluation: Reviewed summary

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Manos Katsomallos 2021-11-29 05:02:09 +01:00
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\section{Summary} \section{Summary}
\label{sec:eval-sum} \label{sec:eval-sum}
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. 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. 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. \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. 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.
% \kat{it would be nice to see it clearly on Figure 5.5. (eg, by including another bar that shows adaptive without landmark selection)} % \kat{it would be nice to see it clearly on Figure 5.5. (eg, by including another bar that shows adaptive without landmark selection)}
% \mk{Done.} % \mk{Done.}
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. 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.