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evaluation: Reviewed thething

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Manos Katsomallos 2021-11-29 04:05:44 +01:00
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@ -21,7 +21,7 @@ We observe that a greater average {\thething}--regular event distance in a time
\label{subsec:lmdk-expt-bgt}
Figure~\ref{fig:real} exhibits the performance of the three schemes, \texttt{Skip}, \texttt{Uniform}, and \texttt{Adaptive} applied on the three data sets that we study.
Notice that, in the cases when we have $0\%$ and $100\%$ of the events being {\thethings}, we get the same behavior as in event- and user-level privacy respectively.
This happens due the fact that at each timestamp we take into account only the data items at the current timestamp and ignore the rest of the time series (event-level) when there are no {\thethings}.
This happens due to the fact that at each timestamp we take into account only the data items at the current timestamp and ignore the rest of the time series (event-level) when there are no {\thethings}.
Whereas, when each timestamp corresponds to a {\thething} we consider and protect all the events throughout the entire series (user-level).
% For the Geolife data set (Figure~\ref{fig:geolife}), Skip has the best performance (measured in Mean Absolute Error, in meters) because it invests the most budget overall at every regular event, by approximating the {\thething} data based on previous releases.
% Due to the data set's high density (every $1$--$5$ seconds or every $5$--$10$ meters per point) approximating constantly has a low impact on the data utility.
@ -84,7 +84,7 @@ result in a more effective budget allocation that would improve the performance
\subsection{Temporal distance and correlation}
\label{subsec:lmdk-expt-cor}
As previously mentioned, temporal correlation is inherent in continuous publishing, and it is the cause of supplementary privacy loss in the case of privacy-preserving time series publishing.
In this section, we are interested in studying the effect that the distance of the {\thethings} from every regular event has on the loss caused under the presence of temporal correlation.
In this section, we are interested in studying the effect that the distance of the {\thethings} from every regular event has on the privacy loss caused under the presence of temporal correlation.
Figure~\ref{fig:avg-dist} shows a comparison of the average temporal distance of the events from the previous/next {\thething} or the start/end of the time series for various distributions in our synthetic data.
More specifically, we model the distance of an event as the count of the total number of events between itself and the nearest {\thething} or the time series edge.
@ -92,7 +92,7 @@ More specifically, we model the distance of an event as the count of the total n
\begin{figure}[htp]
\centering
\includegraphics[width=.5\linewidth]{evaluation/avg-dist}%
\caption{Average temporal distance of regular events from the {\thethings} for different {\thethings} percentages within a time series in various {\thething} distributions.}
\caption{Average temporal distance of regular events from the {\thethings} for different {\thething} percentages within a time series in various {\thething} distributions.}
\label{fig:avg-dist}
\end{figure}