text: Moved publications in the beginning

text/evaluation/main.tex

@@ -1,5 +1,7 @@
 \chapter{Evaluation}
 \label{ch:eval}
+\nnfootnote{This chapter is under review for being published in the proceedings of the $12$th ACM Conference on Data and Application Security and Privacy~\cite{katsomallos2022landmark}.\bigskip}
+
 In this chapter we present the experiments that we performed in order to evaluate {\thething} privacy (Chapter~\ref{ch:lmdk-prv}) on real and synthetic data sets.
 Section~\ref{sec:eval-dtl} contains all the details regarding the data sets the we used for our experiments along with the system configurations.
 Section~\ref{sec:eval-lmdk} evaluates the data utility of the {\thething} privacy schemes that we designed in Section~\ref{sec:thething} and investigates the behavior of the privacy loss under temporal correlation for different distributions of {\thethings}.

text/evaluation/summary.tex

@@ -1,7 +1,5 @@
 \section{Summary}
 \label{sec:eval-sum}
-\nnfootnote{This chapter is under review for being published in the proceedings of the $12$th ACM Conference on Data and Application Security and Privacy~\cite{katsomallos2022landmark}.}
-
 In this chapter we presented the experimental evaluation of the {\thething} privacy schemes and the privacy-preserving {\thething} selection scheme that we developed in Chapter~\ref{ch:lmdk-prv}, on real and synthetic data sets.
 The Adaptive scheme is the most reliable and best performing scheme, in terms of overall data utility, with minimal tuning across most of the cases.
 Skip performs optimally in data sets with a smaller target value range, where approximation fits best.

text/introduction/main.tex

@@ -1,6 +1,6 @@
 \chapter{Introduction}
 \label{ch:intro}
-\nnfootnote{This chapter was presented at the S{\~a}o Paulo School of Advanced Science on Smart Cities~\cite{katsomallos2016measuring}, as well as during the $11$th International Workshop on Information Search, Integration, and Personalization~\cite{kotzinos2016data} and at the DaQuaTa International Workshop~\cite{kotzinos2017data}.}
+\nnfootnote{This chapter was presented during the $11$th International Workshop on Information Search, Integration, and Personalization~\cite{kotzinos2016data} and at the DaQuaTa International Workshop~\cite{kotzinos2017data}, as well as at the S{\~a}o Paulo School of Advanced Science on Smart Cities~\cite{katsomallos2016measuring}.}
 
 Data privacy is becoming an increasingly important issue, both at a technical and at a societal level, and introduces various challenges ranging from the way we share and publish data sets to the way we use online and mobile services.
 Personal information, also described as \emph{microdata}, acquired increasing value and are in many cases used as the `currency'~\cite{economist2016data} to pay for access to various services, i.e.,~users are asked to exchange their personal information with the service provided.

text/problem/main.tex

@@ -1,5 +1,7 @@
 \chapter{{\Thething} privacy}
 \label{ch:lmdk-prv}
+\nnfootnote{This chapter is under review for being published in the proceedings of the $12$th ACM Conference on Data and Application Security and Privacy~\cite{katsomallos2022landmark}.}
+
 % 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.
 % Continuously user-generated data

text/problem/summary.tex

@@ -1,7 +1,5 @@
 \section{Summary}
 \label{sec:lmdk-sum}
-\nnfootnote{This chapter is under review for being published in the proceedings of the $12$th ACM Conference on Data and Application Security and Privacy~\cite{katsomallos2022landmark}.}
-
 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 with respect to the traditional user-level differential privacy.
 We also proposed three models for {\thething} privacy, and quantified the privacy loss under temporal correlation.
 Furthermore, we present three solutions to enhance our privacy scheme by protecting the actual temporal position of the {\thethings} in the time series.

text/related/main.tex

@@ -1,7 +1,9 @@
 \chapter{Related work}
 \label{ch:rel}
+\nnfootnote{This chapter was published in the $19$th Journal of Spatial Information Science~\cite{katsomallos2019privacy}.}
 % \kat{Change the way you introduce the related work chapter; do not list a series of surveys. You should speak about the several directions for privacy-preserving methods (and then citing the surveys if you want). Then, you should focus on the particular configuration that you are interested in (continual observation). Summarize what we will see in the next sections by  giving also the general structure of the chapter.}
 % \mk{Moved to summary}
+
 In this chapter, we survey works that deal with privacy under continuous data publishing covering diverse use cases.
 We present $48$ published articles spanning $16$ years of research from $2006$ to $2021$, with $2015$ being the median, based on two levels of categorization (Figure~\ref{fig:rel-yrs}). 
 % \kat{The related work section of your thesis, should make a connection/comparison to your work. This means that you should position the works presented wrt  your problem and your solution if the problems are the same. Put a small (or big) paragraph in the end of each of the two sections (microdata and statistical data) and name the similarities/differences }