diff --git a/text/problem/main.tex b/text/problem/main.tex
index c97cbd4..5cc12cc 100644
--- a/text/problem/main.tex
+++ b/text/problem/main.tex
@@ -1,4 +1,4 @@
-\chapter{The problem}
+\chapter{Landmark Privacy}
 
 \input{problem/thething/main}
-\input{problem/theotherthing/main}
+
diff --git a/text/problem/theotherthing/main.tex b/text/problem/theotherthing/main.tex
index 4c72eda..83ed973 100644
--- a/text/problem/theotherthing/main.tex
+++ b/text/problem/theotherthing/main.tex
@@ -1,2 +1,2 @@
-\section{Selection of events}
-\label{sec:theotherthing}
+\subsection{Selection of events}
+\label{subsec:theotherthing}
diff --git a/text/problem/thething/contribution.tex b/text/problem/thething/contribution.tex
index 64f16fd..266ff1c 100644
--- a/text/problem/thething/contribution.tex
+++ b/text/problem/thething/contribution.tex
@@ -1,5 +1,5 @@
-\subsection{Contribution}
-\label{subsec:lmdk-contrib}
+\section{Contribution}
+\label{sec:lmdk-contrib}
 
 In this chapter, we formally define a novel privacy notion that we call \emph{{\thething} privacy}.
 We apply this privacy notion to time series consisting of \emph{{\thethings}} and regular events, and we design and implement three {\thething} privacy mechanisms.
diff --git a/text/problem/thething/main.tex b/text/problem/thething/main.tex
index b19fcf5..d4d6095 100644
--- a/text/problem/thething/main.tex
+++ b/text/problem/thething/main.tex
@@ -1,10 +1,14 @@
-\section{Significant events}
-\label{sec:thething}
+%\section{Significant events}
+%\label{sec:thething}
 
 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.}
+We propose two privacy models that guarantee {\thething} privacy.
+To further enhance our privacy method, and protect the landmarks position in the time series, we propose techniques to perturb the initial landmarks set (Section~\ref{sec:theotherthing}). 
+
+% and validate our proposal on real and synthetic data sets. \kat{this will go in the experiments section}
+ 
 \input{problem/thething/motivation}
 \input{problem/thething/contribution}
 \input{problem/thething/problem}
+\input{problem/theotherthing/main}
 \input{problem/thething/summary}
diff --git a/text/problem/thething/motivation.tex b/text/problem/thething/motivation.tex
index 3899f63..7a0d588 100644
--- a/text/problem/thething/motivation.tex
+++ b/text/problem/thething/motivation.tex
@@ -1,5 +1,5 @@
-\subsection{Motivation}
-\label{subsec:lmdk-motiv}
+\section{Motivation}
+\label{sec:lmdk-motiv}
 
 The plethora of sensors currently embedded in 
 or paired with personal devices and other infrastructures have paved the way for the development of numerous \emph{crowdsensing services} (e.g.,~Google Maps~\cite{gmaps}, Waze~\cite{waze}, etc.) based on the collected personal, and usually geotagged and timestamped data.
diff --git a/text/problem/thething/problem.tex b/text/problem/thething/problem.tex
index ecd167c..b193354 100644
--- a/text/problem/thething/problem.tex
+++ b/text/problem/thething/problem.tex
@@ -1,5 +1,5 @@
-\subsection{{\Thething} privacy}
-\label{subsec:lmdk-prob}
+\section{{\Thething} privacy}
+\label{sec:lmdk-prob}
 
 {\Thething} privacy is based on differential privacy. 
 For this reason, we revisit the definition and important properties of differential privacy before moving on to the main ideas of this paper.
diff --git a/text/problem/thething/summary.tex b/text/problem/thething/summary.tex
index f1e9468..225c371 100644
--- a/text/problem/thething/summary.tex
+++ b/text/problem/thething/summary.tex
@@ -1,6 +1,7 @@
-\subsection{Summary and future work}
-\label{subsec:lmdk-sum}
+\section{Summary}
+\label{sec:lmdk-sum}
 In this chapter, we presented \emph{{\thething} privacy} for privacy-preserving time series publishing, which allows for the protection of significant events, while improving the utility of the final result w.r.t. the traditional user-level differential privacy.
 We also proposed three models for  {\thething} privacy, and quantified the privacy loss under temporal correlation.
-Our experiments on real and synthetic data sets validate our proposal. 
-In the future, we aim to investigate privacy-preserving {\thething} selection and propose a mechanism based on user-preferences and semantics.
+%Our experiments on real and synthetic data sets validate our proposal. 
+%In the future, we aim to investigate privacy-preserving {\thething} selection and propose a mechanism based on user-preferences and semantics.
+\kat{Advertise your work! Say what is cool about the work and how it differs from the others! Mention also the summary for selection of events. The discussion for the experiments and future work you postpone for the respective sections, you may though make reference to specific experiments to support your claims.   }
\ No newline at end of file