evaluation: Added sel-eps
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graphics/evaluation/copenhagen-sel-eps.pdf
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graphics/evaluation/copenhagen-sel-eps.pdf
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graphics/evaluation/hue-sel-eps.pdf
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@ -33,6 +33,35 @@ While both methods share the same mean normalized distance of $0.4$, the Euclide
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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}.
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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}.
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\subsection{Privacy budget tuning}
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\label{subsec:sel-eps}
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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.
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Uniform is our baseline implementation, and hence allows us to derive more accurate conclusions in this case.
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In general, greater ratios will result in more accurate, i.e.,~smaller, {\thething} sets and less accurate values in the released data sets.
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\begin{figure}[htp]
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\centering
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\subcaptionbox{Copenhagen\label{fig:copenhagen-sel-eps}}{%
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\includegraphics[width=.5\linewidth]{evaluation/copenhagen-sel-eps}%
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}%
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\hspace{\fill}
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\\ \bigskip
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\subcaptionbox{HUE\label{fig:hue-sel-eps}}{%
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\includegraphics[width=.5\linewidth]{evaluation/hue-sel-eps}%
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}%
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\subcaptionbox{T-drive\label{fig:t-drive-sel-eps}}{%
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\includegraphics[width=.5\linewidth]{evaluation/t-drive-sel-eps}%
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}%
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\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.}
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\label{fig:sel-eps}
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\end{figure}
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The application of the randomized response mechanism, in the Copenhagen data set, is tolerant to the fluctuations of the privacy budget.
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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.
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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.
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\subsection{Budget allocation and {\thething} selection}
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\subsection{Budget allocation and {\thething} selection}
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\label{subsec:sel-prv}
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\label{subsec:sel-prv}
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@ -44,6 +73,7 @@ Figure~\ref{fig:real-sel} exhibits the performance of Skip, Uniform, and Adaptiv
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\includegraphics[width=.5\linewidth]{evaluation/copenhagen-sel}%
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\includegraphics[width=.5\linewidth]{evaluation/copenhagen-sel}%
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}%
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}%
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\hspace{\fill}
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\hspace{\fill}
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\\ \bigskip
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\subcaptionbox{HUE\label{fig:hue-sel}}{%
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\subcaptionbox{HUE\label{fig:hue-sel}}{%
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\includegraphics[width=.5\linewidth]{evaluation/hue-sel}%
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\includegraphics[width=.5\linewidth]{evaluation/hue-sel}%
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}%
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}%
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