Chapters and sections

text/evaluation/main.tex

@@ -0,0 +1,5 @@
+\chapter{Evaluation}
+\label{ch:eval}
+
+\input{evaluation/thething}
+\input{evaluation/theotherthing}

text/evaluation/theotherthing.tex

@@ -0,0 +1,2 @@
+\section{Selection of events}
+\label{sec:lmdk-sel-eval}

text/evaluation/thething.tex

@@ -1,7 +1,8 @@
-\section{Evaluation}
+\section{Significant events}
 \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. }
+% \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. }
+% \mk{OK.}
 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}. 

text/main.tex

@@ -94,8 +94,8 @@
 \input{introduction/main}
 \input{preliminaries/main}
 \input{related/main}
-\input{thething/main}
+\input{problem/main}
-\input{theotherthing/main}
+\input{evaluation/main}
 \input{conclusion/main}
 
 \backmatter

text/problem/main.tex

@@ -0,0 +1,4 @@
+\chapter{The problem}
+
+\input{problem/thething/main}
+\input{problem/theotherthing/main}

text/problem/theotherthing/main.tex

@@ -0,0 +1,2 @@
+\section{Selection of events}
+\label{sec:theotherthing}

text/problem/thething/contribution.tex

@@ -1,5 +1,5 @@
-\section{Contribution}
+\subsection{Contribution}
-\label{sec:lmdk-contrib}
+\label{subsec: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

@@ -1,11 +1,10 @@
-\chapter{Significant events}
+\section{Significant events}
-\label{ch:thething}
+\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.}
-\input{thething/motivation}
+\input{problem/thething/motivation}
-\input{thething/contribution}
+\input{problem/thething/contribution}
-\input{thething/problem}
+\input{problem/thething/problem}
-\input{thething/evaluation}
+\input{problem/thething/summary}
-\input{thething/summary}

text/problem/thething/motivation.tex

@@ -1,5 +1,5 @@
-\section{Motivation}
+\subsection{Motivation}
-\label{sec:lmdk-motiv}
+\label{subsec: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 @@
-\section{{\Thething} privacy}
+\subsection{{\Thething} privacy}
-\label{sec:lmdk-prob}
+\label{subsec: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.
@@ -7,7 +7,7 @@ Although, its local variant~\cite{duchi2013local} is more compatible with microd
 We refer the interested reader to~\cite{desfontaines2020sok} for a systematic taxonomy of the different variants and extensions of differential privacy, to~\cite{katsomallos2019privacy} for a survey of privacy models for continuous data publishing, and to~\cite{primault2018long} for an organization of the recent contributions in location privacy.
 
 
-\subsection{Differential privacy}
+\subsubsection{Differential privacy}
 \label{subsec:dp}
 
 \emph{Differential privacy}~\cite{dwork2006calibrating} is a property of a privacy mechanism $\mathcal{M}$ processing a set of \emph{privacy-sensitive} personal data $D$, 
@@ -55,7 +55,7 @@ results in $\mathcal{M}(D)$ with $\varepsilon = \varepsilon_1 + \varepsilon_2$.
 %Cao et al.~\cite{cao2017quantifying} propose a method for computing the total temporal privacy loss (TPL) in the presence of temporal correlations and background knowledge. Due to the lack of space, we refer the interested reader to the original publication for the complete definitions and formulas.
 
 
-\subsection{Problem description and definition}
+\subsubsection{Problem description and definition}
 \label{subsec:prob-set}
 
 %\kat{move flowchart here}

text/problem/thething/summary.tex

@@ -1,5 +1,5 @@
-\section{Summary and future work}
+\subsection{Summary and future work}
-\label{sec:lmdk-sum}
+\label{subsec: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. 

text/theotherthing/contribution.tex

@@ -1,2 +0,0 @@
-\section{Contribution}
-\label{sec:lmdk-sel-contrib}

text/theotherthing/evaluation.tex

@@ -1,2 +0,0 @@
-\section{Evaluation}
-\label{sec:lmdk-sel-eval}

text/theotherthing/main.tex

@@ -1,8 +0,0 @@
-\chapter{Privacy-preserving event significance}
-\label{ch:theotherthing}
-
-\input{theotherthing/motivation}
-\input{theotherthing/contribution}
-\input{theotherthing/problem}
-\input{theotherthing/evaluation}
-\input{theotherthing/summary}

text/theotherthing/motivation.tex

@@ -1,2 +0,0 @@
-\section{Motivation}
-\label{sec:lmdk-sel-motiv}

text/theotherthing/problem.tex

@@ -1,2 +0,0 @@
-\section{Privacy-preserving {\thething} selection}
-\label{sec:lmdk-sel-prob}

text/theotherthing/summary.tex

@@ -1,2 +0,0 @@
-\section{Summary and future work}
-\label{sec:lmdk-sel-sum}