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 \section{Summary}
 \label{sec:lmdk-sum}
-\nnfootnote{This chapter was published in the proceedings of the $19$th Journal of Spatial Information Science~\cite{katsomallos2019privacy}.}
+\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.