\section{Summary}
\label{sec: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. 
%In the future, we aim to investigate privacy-preserving {\thething} selection and propose a mechanism based on user-preferences and semantics.
\kat{Advertise your work! Say what is cool about the work and how it differs from the others! Mention also the summary for selection of events. The discussion for the experiments and future work you postpone for the respective sections, you may though make reference to specific experiments to support your claims.   }
