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Manos Katsomallos 2021-10-14 18:18:23 +02:00
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text/problem/summary.tex
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In this chapter, we presented \emph{{\thething} privacy} for privacy-preserving time series publishing, which allows for the protection of significant events, while improving the utility of the final result with respect to the traditional user-level differential privacy.
We also proposed three models for {\thething} privacy, and quantified the privacy loss under temporal correlation.
Furthermore, we present three solutions to enhance our privacy scheme by protecting the actual temporal position of the{\thethings} in the time series.
Furthermore, we present three solutions to enhance our privacy scheme by protecting the actual temporal position of the {\thethings} in the time series.
We differ the experimental evaluation of our methodology to Chapter~\ref{ch:eval} we experiment with real and synthetic data sets to demonstrate the applicability of the {\thething} privacy models by themselves (Section~\ref{sec:eval-lmdk-sel}) and in combination with the {\thething} selection component (Section~\ref{sec:eval-lmdk}).
%Our experiments on real and synthetic data sets validate our proposal.

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text/problem/thething/main.tex
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@ -6,7 +6,7 @@ In reality, this is a simplistic\kat{I would not say simplistic, but unrealistic
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}, 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} in timeseries can be done in an automatic or manual way.
Identifying {\thethings} in time series 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}.
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.