introduction for 3.1.microdata

text/related/main.tex

@@ -16,7 +16,9 @@ Finally, there are some surveys on application-specific privacy challenges.
 For example, Zhou et al.~\cite{zhou2008brief} have a focus on social networks, and Christin et al.~\cite{christin2011survey} give an outline of how privacy aspects are addressed in crowdsensing applications.
 
 In this chapter, we document works that deal with privacy under continuous data publishing covering diverse use cases. 
-We present the works in the literature based on two levels of categorisation. First we group works w.r.t. whether they receive as input microdata or statistical data (see Section~\ref{subsec:data-categories} for the definitions). Then, we further group them into two subcategories, whether they are designed for the finite or infinite (see Section.~\ref{subsec:data-publishing}) observation setting.  \kat{continue}
+We present the works in the literature based on two levels of categorisation. 
+First, we group works w.r.t. whether they receive microdata or statistical data (see Section~\ref{subsec:data-categories} for the definitions) as input.
+Then, we further group them into two subcategories, whether they are designed for the finite or infinite (see Section.~\ref{subsec:data-publishing}) observation setting.  \kat{continue}
 
 %Such a documentation becomes very useful nowadays, due to the abundance of continuously user-generated data sets that could be analyzed and/or published in a privacy-preserving way, and the quick progress made in this research field.
 

text/related/micro.tex

@@ -1,14 +1,21 @@
 \section{Microdata}
 \label{sec:micro}
 
-As observed in Table~\ref{tab:micro}, privacy-preserving algorithms for microdata rely mostly on $k$-anonymity or derivatives of it.
+\kat{Table 1 must be properly introduced in the text, and also commented on (derive all the conclusions from it, instead of reporting one conclusion randomly here).}
+Table~\ref{tab:micro} summarizes the literature for the Microdata category. 
+Each reviewed work is abstractly described in this table, by its category (finite or infintite), its publishing mode (batch or streaming) and scheme(global or local), the level of privacy achieved (user, event, w-event), the attacks addressed, the privacy operation applied, and the base method it is built upon.
+We observe that privacy-preserving algorithms for microdata rely mostly on $k$-anonymity or derivatives of it.
 Ganta et al.~\cite{ganta2008composition} revealed that $k$-anonymity methods are vulnerable to complementary release attacks (or \emph{composition attacks} in the original publication).
 Consequently, the research community proposed solutions based on $k$-anonymity, focusing on different threats linked to continuous publication, as we review later on.
 However, notice that only a couple~\cite{li2016hybrid,shmueli2015privacy}
 of the following works assume that data sets are privacy-protected \emph{independently} of one another, meaning that the publisher is oblivious of the rest of the publications. 
-On the other side, algorithms that are based on differential privacy are not concerned with so specific attacks as, by definition, differential privacy considers that the adversary may possess any kind of background knowledge. 
-Later on, data dependencies were also considered for differential privacy algorithms, to account for the extra privacy loss entailed by them.
+On the other side, algorithms based on differential privacy are not concerned with so specific attacks as, by definition, differential privacy considers that the adversary may possess any kind of background knowledge. 
+Moreover, more recent works consider also data dependencies 
+%are considered for differential privacy algorithms, 
+to account for the extra privacy loss entailed by them.
+\bigskip
 
+Next, we begin the discussion with the works designed for microdata as finite observations.
 \includetable{micro}