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text/evaluation/theotherthing.tex
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\section{Selection of events}
\section{Selection of landmarks}
\label{sec:eval-lmdk-sel}
In this section, we present the experiments that we performed, to test the methodology that we presented in Section~\ref{subsec:lmdk-sel-sol}, on real and synthetic data sets.
With the experiments on the synthetic data sets (Section~\ref{subsec:sel-utl}) we show the normalized Euclidean and Wasserstein distances of the time series histograms for various distributions and {\thething} percentages.
In this section, we present the experiments on the methodology for the {\thethings} selection presented in Section~\ref{subsec:lmdk-sel-sol}, on the real and the synthetic data sets.
With the experiments on the synthetic data sets (Section~\ref{subsec:sel-utl}) we show the normalized Euclidean and Wasserstein distances \kat{is this distance the landmark distance that we saw just before ? clarify } of the time series histograms for various distributions and {\thething} percentages.
This allows us to justify our design decisions for our concept that we showcased in Section~\ref{subsec:lmdk-sel-sol}.
With the experiments on the real data sets (Section~\ref{subsec:sel-prv}), we show the performance in terms of utility of our three {\thething} mechanisms in combination with the privacy preserving {\thething} selection component.
\kat{Mention whether it improves the original proposal or not.}
\subsection{{\Thething} selection utility metrics}
@ -54,8 +55,10 @@ Figure~\ref{fig:real-sel} exhibits the performance of Skip, Uniform, and Adaptiv
\end{figure}
In comparison with the utility performance without the {\thething} selection component (Figure~\ref{fig:real}), we notice a slight deterioration for all three models.
This is natural since we allocated part of the available privacy budget to the privacy-preserving {\thething} selection component which in turn increased the number of {\thethings}.
This is natural since we allocated part of the available privacy budget to the privacy-preserving {\thething} selection component, which in turn increased the number of {\thethings}.
Therefore, there is less privacy budget available for data publishing throughout the time series for $0$\% and $100$\% {\thethings}.
Skip performs best in our experiments with HUE, due to the low range in the energy consumption and the high scale of the Laplace noise which it avoids due to its tendency to approximate.
However, for the Copenhagen data set and T-drive it attains greater mean absolute error than the user-level protection scheme.
Overall, Adaptive has a consistent performance in terms of utility for all of the data sets that we experimented with.
\kat{why not for the other percentages?}
Skip performs best in our experiments with HUE, due to the low range in the energy consumption and the high scale of the Laplace noise, which it avoids due to the employed approximation.
However, for the Copenhagen data set and T-drive Skip attains greater mean absolute error than the user-level protection scheme, which exposes no benefit w.r.t. user-level protection.
Overall, Adaptive has a consistent performance in terms of utility for all of the data sets that we experimented with, and always outperforms the user-level privacy.
Thus, it is selected as the best mechanism to use in general.