privacy: Intro and minor corrections
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\section{Data privacy}
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\label{sec:privacy}
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In this section we first study the notion of information disclosure and focus on the privacy attacks that can lead to it.
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Furthermore, we investigate the possible privacy protection levels in continuous data publishing.
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Finally, we identify the most common privacy operations and the seminal works for privacy-preserving data publishing.
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\subsection{Information disclosure}
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\label{subsec:prv-info-dscl}
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When personal data are publicly released, either as microdata or statistical data, individuals' privacy can be compromised, i.e,~an adversary becomes certain about an individual's \emph{sensitive attribute}, i.e.,~personal information, with a probability higher than a desired threshold.
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In the literature, this incident
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% compromise
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% \kat{do you want to say 'peril', 'risk' instead of compromise ?}
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% \mk{No, it's more about the result, i.e., compromising privacy, rather than the risk.}
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is known as \emph{information disclosure} and is usually categorized as (\cite{li2007t, wang2010privacy, narayanan2008robust}):
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is known as \emph{information disclosure} and is usually categorized~\cite{li2007t, wang2010privacy, narayanan2008robust} as:
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% \emph{presence}, \emph{identity}, or \emph{attribute} disclosure.
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\begin{itemize}
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\item \emph{Presence disclosure}---the participation or absence of an individual in a data set is revealed.
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\item \emph{Identity disclosure}---an individual is linked to a particular record.
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\item \emph{Attribute disclosure}---new information (attribute value) about an individual is revealed.
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\item \emph{Presence disclosure} takes place when the participation or absence of an individual in a data set is revealed.
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\item \emph{Identity disclosure} links an individual to a particular record.
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\item \emph{Attribute disclosure} reveals information (attribute value) about an individual.
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\end{itemize}
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In the literature, identity disclosure is also referred to as \emph{record linkage}, and presence disclosure as \emph{table linkage}.
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@ -28,7 +32,6 @@ Attribute disclosure appears when it is revealed from (a privacy-protected versi
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\subsection{Attacks to privacy}
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\label{subsec:prv-attacks}
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Information disclosure is typically achieved by combining supplementary (background) knowledge with the released data or by setting unrealistic assumptions while designing the privacy-preserving algorithms.
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In its general form, this is known as \emph{adversarial} or \emph{linkage} attack.
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Even though many works directly refer to the general category of linkage attacks, we distinguish also the following sub-categories:
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@ -76,7 +79,8 @@ Data publishers typically release events in the form of sequences of data items,
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We use the term `users' to refer to the \emph{individuals}, also known as \emph{participants}, who are the source of the processed and published data.
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Therefore, they should not be confused with the consumers of the released data sets.
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Users are subject to privacy attacks, and thus are the main point of interest of privacy protection mechanisms.
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The possible privacy protection levels are the \emph{event}~\cite{dwork2010differential, dwork2010pan}, \emph{user}~\cite{dwork2010differential, dwork2010pan}, and \emph{$w$-event}~\cite{kellaris2014differentially}.
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The possible privacy protection levels are:
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% the \emph{event}~\cite{dwork2010differential, dwork2010pan}, \emph{user}~\cite{dwork2010differential, dwork2010pan}, and \emph{$w$-event}~\cite{kellaris2014differentially}.
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\begin{enumerate}[(a)]
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\item \emph{Event-level}~\cite{dwork2010differential, dwork2010pan} limits the privacy protection to \emph{any single event} in a time series, providing high
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