chapter 3. done

text/related/micro.tex

@@ -1,7 +1,7 @@
 \section{Microdata}
 \label{sec:micro}
 
-\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.
@@ -15,7 +15,7 @@ Moreover, more recent works consider also data dependencies
 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.
+We begin the discussion with the works designed for microdata as finite observations (Section~\ref{subsec:micro-finite}), to continue with the infinite observations setting (Section~\ref{subsec:micro-infinite}).
 \includetable{micro}
 
 

text/related/statistical.tex

@@ -1,10 +1,14 @@
 \section{Statistical data}
 \label{sec:statistical}
 
-When continuously publishing statistical data, usually in the form of counts, the most widely used privacy method is differential privacy, or derivatives of it, as witnessed in Table~\ref{tab:statistical}.
+As in Section~\ref{sec:micro}, we summarize the literature  for the Statistical Data category in Table~\ref{tab:statistical}, which we structure identically as Table~\ref{tab:micro}.
-In theory differential privacy makes no assumptions about the background knowledge available to the adversary.
+For a reminder, 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.
-In practice, as we observe in Table~\ref{tab:statistical}, data dependencies (e.g.,~correlations) arising in the continuous publication setting are frequently (but without it being the rule) considered as attacks in the proposed algorithms.
 
+As witnessed in Table~\ref{tab:statistical}, when continuously publishing statistical data, usually in the form of counts, the most widely used privacy method is differential privacy, or derivatives of it.
+In theory differential privacy makes no assumptions about the background knowledge available to the adversary.
+In practice, data dependencies (e.g.,~correlations) arising in the continuous publication setting are frequently (but without it being the rule) considered as attacks in the proposed algorithms.
+
+We begin the discussion with the works designed for microdata as finite observations (Section~\ref{subsec:statistical-finite}), to continue with the infinite observations setting (Section~\ref{subsec:statistical-infinite}).
 \includetable{statistical}
 
 
@@ -424,3 +428,5 @@ Increasing the discount factor offers stronger privacy protection, equivalent to
 Whereas, increasing the discount coefficient resembles the behavior of event-level differential privacy.
 Selecting a suitable value for the privacy budget and the discount parameter allows for bounding the overall privacy loss in an infinite observation scenario.
 However, the assumption that all users discount previous data releases limits the applicability of the the current scheme in real-world scenarios for statistical data.
+
+\kat{Add here a paragraph that contrasts/compares your work with the works presented for statistical data.}

text/related/summary.tex

@@ -2,3 +2,4 @@
 \label{sec:sum-rel}
 
 This is the summary of this chapter.
+\kat{? Don't forget to mention here the publication that you have.}