introduction: Moved example here

text/introduction/main.tex

@@ -68,5 +68,18 @@ Typically, in such cases, we have a collection of data referring to the same ind
 Additionally, in many cases, the privacy-preserving processes should take into account implicit correlations and restrictions that exist, e.g.,~space-imposed collocation or movement restrictions.
 Since these data are related to most of the important applications and services that enjoy high utilization rates, privacy-preserving continuous data publishing becomes one of the emblematic problems of our time.
 
+To accompany and facilitate the descriptions in this chapter, we provide the following running example.
+
+\begin{example}
+  \label{ex:snapshot}
+  Users interact with an LBS by making queries in order to retrieve some useful location-based information or just reporting user-state at various locations.
+  This user--LBS interaction generates user-related data, organized in a  schema with the following attributes: \emph{Name} (the unique identifier of the table), \emph{Age}, \emph{Location}, and \emph{Status} (Table~\ref{tab:snapshot-micro}).
+  The `Status' attribute includes information that characterizes the user's state or the query itself, and its value varies according to the service functionality.
+  Subsequently, the generated data are aggregated (by issuing count queries over them) in order to derive useful information about the popularity of the venues during the day (Table~\ref{tab:snapshot-statistical}).
+
+  \includetable{snapshot}
+
+\end{example}
+
 \input{introduction/contribution}
 \input{introduction/structure}

text/preliminaries/main.tex

@@ -4,20 +4,7 @@
 In this chapter, we introduce some relevant terminology and background knowledge around the problem of continuous publishing of sensitive data sets.
 First, we categorize data as we view them in the context of continuous data publishing.
 Second, we define data privacy, we list the kinds of attacks that have been identified in the literature, as well as the desired privacy levels that can be achieved, and the basic privacy operations that are applied to achieve data privacy.
-Third, we provide a brief overview of the seminal works on privacy-preserving data publishing, used also in continuous data publishing, fundamental in the domain and important for the understanding of the rest of the survey.
-
-To accompany and facilitate the descriptions in this chapter, we provide the following running example.
-
-\begin{example}
-  \label{ex:snapshot}
-  Users interact with an LBS by making queries in order to retrieve some useful location-based information or just reporting user-state at various locations.
-  This user--LBS interaction generates user-related data, organized in a  schema with the following attributes: \emph{Name} (the unique identifier of the table), \emph{Age}, \emph{Location}, and \emph{Status} (Table~\ref{tab:snapshot-micro}).
-  The `Status' attribute includes information that characterizes the user's state or the query itself, and its value varies according to the service functionality.
-  Subsequently, the generated data are aggregated (by issuing count queries over them) in order to derive useful information about the popularity of the venues during the day (Table~\ref{tab:snapshot-statistical}).
-
-  \includetable{snapshot}
-
-\end{example}
+Third, we provide a brief overview of the seminal works on privacy-preserving data publishing, used also in continuous data publishing, fundamental in the domain and important for the understanding of the rest of the chapter.
 
 \input{preliminaries/data}
 \input{preliminaries/privacy}