Merge branch 'master'

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

@@ -1,6 +1,7 @@
-\section{Significant events}
-\label{sec:thething}
+%\section{Significant events}
+%\label{sec:thething}
 
+<<<<<<< HEAD
 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 three 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.}
@@ -8,4 +9,16 @@ We propose three privacy models that guarantee {\thething} privacy and validate
 \input{problem/thething/contribution}
 \input{problem/thething/problem}
 \input{problem/thething/solution}
+=======
+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.
+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}
+>>>>>>> b334e056b320357ce4f4eaa89a1be7f3576350cf
 \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}
 
 % Crowdsensing applications
 The plethora of sensors currently embedded in personal devices and other infrastructures have paved the way for the development of numerous \emph{crowdsensing services} (e.g.,~Ring~\cite{ring}, TousAntiCovid~\cite{tousanticovid}, Waze~\cite{waze}, etc.) based on the collected personal, and usually geotagged and timestamped data.

text/problem/thething/problem.tex

@@ -1,5 +1,10 @@
+<<<<<<< HEAD
 \subsection{Problem description and definition}
 \label{subsec:lmdk-prob}
+=======
+\section{{\Thething} privacy}
+\label{sec:lmdk-prob}
+>>>>>>> b334e056b320357ce4f4eaa89a1be7f3576350cf
 
 Our problem setting consists of three entities: (i) data generators (users), (ii) data publishers (trusted non-adversarial entities), and (iii) data consumers (possibly adversarial entities). 
 Users generate sensitive data, which are processed in a secure and private way by a trusted curator and are later published in order to be consumed by potentially adversarial data analysts. 

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.   }