diff --git a/text/preliminaries/privacy.tex b/text/preliminaries/privacy.tex
index 03b98a3..06f7142 100644
--- a/text/preliminaries/privacy.tex
+++ b/text/preliminaries/privacy.tex
@@ -158,6 +158,7 @@ For these reasons, there will be no further discussion around this family of tec
 
 \subsection{Seminal works}
 \label{subsec:prv-seminal}
+\kat{Seminal works fit best in the related work section}
 
 For completeness, in this section we present the seminal works for privacy-preserving data publishing, which, even though originally designed for the snapshot publishing scenario, have paved the way, since many of the works in privacy-preserving continuous publishing are based on or extend them.
 
diff --git a/text/related/main.tex b/text/related/main.tex
index 0b99503..42db67b 100644
--- a/text/related/main.tex
+++ b/text/related/main.tex
@@ -1,6 +1,8 @@
 \chapter{Related work}
 \label{ch:rel}
 
+\kat{Be sure to update this chapter with more recent works, after the survey was published.. Moreover, the introduction here must be updated, we are not talking about a survey anymore but for your thesis. This means that possibly you need to add a section about some general privacy techniques, which go beyond the continuous publication scenario.}
+
 Since the domain of data privacy is vast, several surveys have already been published with different scopes.
 A group of surveys focuses on specific different families of privacy-preserving algorithms and techniques.
 For instance, Simi et al.~\cite{simi2017extensive} provide an extensive study of works on $k$-anonymity and Dwork~\cite{dwork2008differential} focuses on differential privacy.
diff --git a/text/thething/evaluation.tex b/text/thething/evaluation.tex
index a114449..f44fafb 100644
--- a/text/thething/evaluation.tex
+++ b/text/thething/evaluation.tex
@@ -1,6 +1,7 @@
 \section{Evaluation}
 \label{sec:lmdk-eval}
 
+\kat{After discussing with Dimitris, I thought you are keeping one chapter for the proposals of the thesis. In this case, it would be more clean to keep the theoretical contributions in one chapter and the evaluation in a separate chapter. }
 In this section we present the experiments that we performed on real and synthetic data sets. 
 With the experiments on the synthetic data sets we show the privacy loss by our framework when tuning the size and statistical characteristics of the input {\thething} set $L$.
 We also show how the privacy loss under temporal correlation is affected by the number and distribution of the {\thethings}. 
diff --git a/text/thething/main.tex b/text/thething/main.tex
index b78bab3..dd73547 100644
--- a/text/thething/main.tex
+++ b/text/thething/main.tex
@@ -3,7 +3,7 @@
 
 In this chapter, we propose a novel configurable privacy scheme, \emph{\thething} privacy, which takes into account significant events (\emph{\thethings}) in the time series and allocates the available privacy budget accordingly.
 We propose two privacy models that guarantee {\thething} privacy and validate our proposal on real and synthetic data sets.
-
+\kat{Now, you have space so you need to be more detailed in the discussions, the motivation, the examples etc.}
 \input{thething/motivation}
 \input{thething/contribution}
 \input{thething/problem}
diff --git a/text/titlepage.tex b/text/titlepage.tex
index 4ef970b..b1f4a50 100644
--- a/text/titlepage.tex
+++ b/text/titlepage.tex
@@ -27,7 +27,7 @@
   {acceptée sur proposition du jury :\\
   \vspace{1em}
   Prof. Dimitris Kotzinos, directeur de thèse\\
-  Prof. Katerina Tzompanaki, co-encadrante de thèse\\
+  McF. Katerina Tzompanaki, co-encadrante de thèse\\
   Dr. *** ***, rapporteur\\
   Dr. *** ***, examinateur\\}
   \vfill