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Quack!

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Manos Katsomallos 2022-01-07 06:09:10 +01:00
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graphics/problem/lmdk-scenario.pdf
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text/problem/main.tex
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@ -7,13 +7,13 @@ The plethora of sensors currently embedded in personal devices and other infrast
% Continuously user-generated data
User--service interactions gather personal event-like data, which are tuples of an identifying attribute of an individual and the---possibly sensitive---information with a timestamp
%(including contextual information),
e.g.,~(\emph{`Bob', `dining', `Canal Saint-Martin', $17{:}00$}).
e.g.,~(\emph{`Quackmore', `dining', `Canal Saint-Martin', $17{:}00$}).
When the interactions are performed in a continuous manner, we obtain ~\emph{time series} of events.
Example~\ref{ex:scenario} is an example of a user--service interaction that results in retrieving location-based information or reporting user-state at various locations.
\begin{example}
\label{ex:scenario}
Figure~\ref{fig:lmdk-scenario} shows a finite sequence of spatiotemporal data, generated by Bob, during an interval of $8$ timestamps.
Figure~\ref{fig:lmdk-scenario} shows a finite sequence of spatiotemporal data, generated by Quackmore, during an interval of $8$ timestamps.
Events in gray correspond to
% privacy-sensitive
% \kat{You should not say that only significant events are privacy-sensitive, because then why put noise to the normal timestamps? Maybe say directly significant for the shaded events?}

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text/problem/thething/main.tex
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@ -36,7 +36,7 @@ Hence, at any timestamp we achieve an overall privacy protection bounded by $\va
\begin{example}
\label{ex:st-cont}
Continuing Example~\ref{ex:scenario}, Bob cares about protecting his {\thethings} ($p_1$, $p_3$, $p_5$, $p_8$) along with every release that he makes, however he is not equally interested for the other regular events in his trajectory.
Continuing Example~\ref{ex:scenario}, Quackmore cares about protecting his {\thethings} ($p_1$, $p_3$, $p_5$, $p_8$) along with every release that he makes, however he is not equally interested for the other regular events in his trajectory.
More technically, he cares about allocating a total budget of $\varepsilon$ on any set of timestamps containing the {\thethings} and one regular event.
Event-level protection is not suitable for this case, since it can only protect one event at a time.
So, let us assume that we apply user-level privacy\footnote{In this scenario, in order to protect all the {\thethings} from timestamp $1$ to $8$, $w$ must be set to $8$, which makes $w$-event privacy equivalent to user-level.}, by distributing equal portions of $\varepsilon$ to all the events, i.e.,~$\frac{\varepsilon}{8}$ to each one (see Figure~\ref{fig:st-cont}).