evaluation: Added sel-eps

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Manos Katsomallos 2021-10-13 09:00:23 +02:00
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@ -33,6 +33,35 @@ While both methods share the same mean normalized distance of $0.4$, the Euclide
Therefore, we choose to utilize the Euclidean distance metric for the implementation of the privacy-preserving {\thething} selection in Section~\ref{subsec:lmdk-sel-sol}. Therefore, we choose to utilize the Euclidean distance metric for the implementation of the privacy-preserving {\thething} selection in Section~\ref{subsec:lmdk-sel-sol}.
\subsection{Privacy budget tuning}
\label{subsec:sel-eps}
In Figure~\ref{fig:sel-eps} we test the Uniform model in real data by investing different ratios ($1$\%, $10$\%, $25$\%, and $50$\%) of the available privacy budget $\varepsilon$ in the {\thething} selection mechanism, in order to figure out the optimal ratio value.
Uniform is our baseline implementation, and hence allows us to derive more accurate conclusions in this case.
In general, greater ratios will result in more accurate, i.e.,~smaller, {\thething} sets and less accurate values in the released data sets.
\begin{figure}[htp]
\centering
\subcaptionbox{Copenhagen\label{fig:copenhagen-sel-eps}}{%
\includegraphics[width=.5\linewidth]{evaluation/copenhagen-sel-eps}%
}%
\hspace{\fill}
\\ \bigskip
\subcaptionbox{HUE\label{fig:hue-sel-eps}}{%
\includegraphics[width=.5\linewidth]{evaluation/hue-sel-eps}%
}%
\subcaptionbox{T-drive\label{fig:t-drive-sel-eps}}{%
\includegraphics[width=.5\linewidth]{evaluation/t-drive-sel-eps}%
}%
\caption{The mean absolute error (a)~as a percentage, (b)~in kWh, and (c)~in meters of the released data for different {\thething} percentages. We apply the Uniform {\thething} privacy model and vary the ratio of the privacy budget $\varepsilon$ that we allocate to the {\thething} selection component.}
\label{fig:sel-eps}
\end{figure}
The application of the randomized response mechanism, in the Copenhagen data set, is tolerant to the fluctuations of the privacy budget.
For HUE and T-drive, we observe that our implementation performs better for lower ratios, e.g.,~$0.01$, where we end up allocating the majority of the available privacy budget to the data release process instead of the {\thething} selection component.
The results of this experiment indicate that we can safely allocate the majority of $\varepsilon$ for publishing the data values, and therefore achieve better data utility, while providing more robust privacy protection to the {\thething} timestamp set.
\subsection{Budget allocation and {\thething} selection} \subsection{Budget allocation and {\thething} selection}
\label{subsec:sel-prv} \label{subsec:sel-prv}
@ -44,6 +73,7 @@ Figure~\ref{fig:real-sel} exhibits the performance of Skip, Uniform, and Adaptiv
\includegraphics[width=.5\linewidth]{evaluation/copenhagen-sel}% \includegraphics[width=.5\linewidth]{evaluation/copenhagen-sel}%
}% }%
\hspace{\fill} \hspace{\fill}
\\ \bigskip
\subcaptionbox{HUE\label{fig:hue-sel}}{% \subcaptionbox{HUE\label{fig:hue-sel}}{%
\includegraphics[width=.5\linewidth]{evaluation/hue-sel}% \includegraphics[width=.5\linewidth]{evaluation/hue-sel}%
}% }%