text: Moved publications in the beginning

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.