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text/problem/thething/main.tex
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\section{Significant events} \section{Significant events}
\label{sec:thething} \label{sec:thething}
The privacy mechanisms for the aforementioned levels assume that in a time series any single event, or any sequence of events, or the entire series of events is equally privacy-significant for the users. The privacy mechanisms for the user, w-event and event levels that are already proposed in the literature, assume that in a time series any single event, or any sequence of events, or the entire series of events is equally privacy-significant for the users.
In reality, this is an simplistic assumption. In reality, this is a simplistic\kat{I would not say simplistic, but unrealistic assumption that deteriorates unnecessarily the quality of the perturbed data} assumption.
The significance of an event is related to certain user-defined privacy criteria, or to its adjacent events, as well as to the entire time series. The fact that an event is significant, can be related to certain user-defined privacy criteria, or to its adjacent events, as well as to the entire time series.
We term significant events as \emph{{\thething} events} or simply \emph{\thethings}. We term significant events as \emph{{\thething} events} or simply \emph{\thethings}, following relevant literature\kat{can you find some other work that uses the same term? otherwise one can raise the question why not ot use the word significant }.
Identifying {\thethings} can be done in an automatic or manual way (but is out of scope for this work).
Identifying {\thethings} in timeseries can be done in an automatic or manual way.
For example, in spatiotemporal data, \emph{places where an individual spent some time} denote \emph{points of interest} (POIs) (called also stay points)~\cite{zheng2015trajectory}. For example, in spatiotemporal data, \emph{places where an individual spent some time} denote \emph{points of interest} (POIs) (called also stay points)~\cite{zheng2015trajectory}.
Such events, and more particularly their spatial attribute values, can be less privacy-sensitive~\cite{primault2018long}, e.g.,~parks, theaters, etc. or, if individuals frequent them, they can reveal supplementary information, e.g.,~residences (home addresses)~\cite{gambs2010show}, places of worship (religious beliefs)~\cite{franceschi-bicchierairussell2015redditor}, etc. Such events, and more particularly their spatial attribute values, can be less privacy-sensitive~\cite{primault2018long}, e.g.,~parks, theaters, etc. or, if individuals frequent them, they can reveal supplementary information, e.g.,~residences (home addresses)~\cite{gambs2010show}, places of worship (religious beliefs)~\cite{franceschi-bicchierairussell2015redditor}, etc.
POIs can be an example of how we can choose {\thethings}, but the idea is not limited to these. POIs can be an example of how we can choose {\thethings}, but the idea is not limited to these.
Another example is the detection of privacy-sensitive user interactions by \emph{contact tracing} applications. Another example is the detection of privacy-sensitive user interactions by \emph{contact tracing} applications.
This can be practical in decease control~\cite{eames2003contact}, similar to the recent outbreak of the Coronavirus disease 2019 (COVID-19) epidemic~\cite{ahmed2020survey}. This can be practical in decease control~\cite{eames2003contact}, similar to the recent outbreak of the Coronavirus disease 2019 (COVID-19) epidemic~\cite{ahmed2020survey}.
Last but not least, {\thethings} in \emph{smart grid} electricity usage patterns could not only reveal the energy consumption of a user but also information regarding activities, e.g.,~`at work', `sleeping', etc. and types of appliances already installed or recently purchased~\cite{khurana2010smart}. Last but not least, {\thethings} in \emph{smart grid} electricity usage patterns could not only reveal the energy consumption of a user but also information regarding activities, e.g.,~`at work', `sleeping', etc. and types of appliances already installed or recently purchased~\cite{khurana2010smart}.
We stress out that {\thething} identification is an orthogonal problem to ours, and that we consider {\thethings} given as input to our problem.
\begin{example} \begin{example}
\label{ex:st-cont} \label{ex:st-cont}