diff --git a/text/preliminaries/privacy.tex b/text/preliminaries/privacy.tex
index 56e3b25..5975e09 100644
--- a/text/preliminaries/privacy.tex
+++ b/text/preliminaries/privacy.tex
@@ -131,7 +131,12 @@ For completeness, in this section we present the seminal works for privacy-prese
 Sweeney coined \emph{$k$-anonymity}~\cite{sweeney2002k}, one of the first established works on data privacy.
 A released data set features $k$-anonymity protection when the sequence of values for a set of identifying attributes, called the \emph{quasi-identifiers}, is the same for at least $k$ records in the data set.
 Computing the quasi-identifiers in a set of attributes is still a hard problem on its own~\cite{motwani2007efficient}.
-$k$-anonymity is syntactic\kat{meaning?}, it constitutes an individual indistinguishable from at least $k-1$ other individuals in the same data set.\kat{you just said this in another way,two sentences before}
+% $k$-anonymity
+% is syntactic,
+% \kat{meaning?}
+% it
+% constitutes an individual indistinguishable from at least $k-1$ other individuals in the same data set.
+% \kat{you just said this in another way,two sentences before}
 In a follow-up work~\cite{sweeney2002achieving}, the author describes a way to achieve $k$-anonymity for a data set by the suppression or generalization of certain values of the quasi-identifiers.
 
 Several works identified and addressed privacy concerns on $k$-anonymity. Machanavajjhala et al.~\cite{machanavajjhala2006diversity} pointed out that $k$-anonymity is vulnerable to homogeneity and background knowledge attacks.
@@ -146,7 +151,8 @@ A data set features  $\theta$-closeness when all of its groups satisfy $\theta$-
 The main drawback of $k$-anonymity (and its derivatives) is that it is not tolerant to external attacks of re-identification on the released data set.
 The problems identified in~\cite{sweeney2002k} appear when attempting to apply $k$-anonymity on continuous data publishing (as we will also see next in Section~\ref{sec:micro}).
 These attacks include multiple $k$-anonymous data set releases with the same record order, subsequent releases of a data set without taking into account previous $k$-anonymous releases, and tuple updates.
-Proposed solutions include rearranging the attributes, setting the whole attribute set of previously released data sets as quasi-identifiers or releasing data based on previous $k$-anonymous releases.\kat{and the citations of these solutions?}
+Proposed solutions include rearranging the attributes, setting the whole attribute set of previously released data sets as quasi-identifiers or releasing data based on previous $k$-anonymous releases~\cite{simi2017extensive}.
+% \kat{and the citations of these solutions?}
 
 
 \subsubsection{Statistical data}