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.