general comments and restructure of section 4

text/problem/main.tex

@@ -1,4 +1,4 @@
-\chapter{The problem}
+\chapter{Landmark Privacy}
 
 \input{problem/thething/main}
-\input{problem/theotherthing/main}
+

text/problem/theotherthing/main.tex

@@ -1,2 +1,2 @@
-\section{Selection of events}
-\label{sec:theotherthing}
+\subsection{Selection of events}
+\label{subsec:theotherthing}

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.

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}

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.

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.

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.   }