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evaluation: Reviewed theotherthing

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Manos Katsomallos 2021-11-29 04:59:21 +01:00
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@ -36,7 +36,7 @@ Thus, we choose to utilize the Euclidean distance metric for the implementation
\subsection{Privacy budget tuning}
\label{subsec:sel-eps}
In Figure~\ref{fig:sel-eps}, we test the \texttt{Uniform} mechanism in real data by investing different ratios ($1$\%, $10$\%, $25$\%, and $50$\%) of the available privacy budget $\varepsilon$ in the dummy {\thething} selection module and the remaining to perturbing the original data values, in order to figure out the optimal ratio value.
In Figure~\ref{fig:sel-eps}, we test the \texttt{Uniform} mechanism with real data by investing different ratios ($1$\%, $10$\%, $25$\%, and $50$\%) of the available privacy budget $\varepsilon$ in the dummy {\thething} selection module and the remaining in perturbing the original data values, in order to figure out the optimal ratio value.
\texttt{Uniform} is our baseline implementation, and hence allows us to derive more accurate conclusions in this case.
In general, we are expecting to observe that greater ratios will result in more accurate, i.e.,~smaller, {\thething} sets and less accurate values in the released data.
@ -65,7 +65,7 @@ The results of this experiment indicate that we can safely allocate the majority
\subsection{Privacy schemes and dummy {\thething} selection}
\label{subsec:sel-prv}
Figure~\ref{fig:real-sel} exhibits the performance of Skip, Uniform, and Adaptive schemes (presented in detail in Section~\ref{subsec:lmdk-mechs}) in combination with the {\thething} selection module (Section~\ref{subsec:lmdk-sel-sol}).
Figure~\ref{fig:real-sel} exhibits the performance of \texttt{Skip}, \texttt{Uniform}, and \texttt{Adaptive} schemes (presented in detail in Section~\ref{subsec:lmdk-mechs}) in combination with the {\thething} selection module (Section~\ref{subsec:lmdk-sel-sol}).
\begin{figure}[htp]
\centering