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 \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 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.
+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 compared to  user-level differential privacy.
+We proposed three schemes for {\thething} privacy, and quantified the privacy loss under temporal correlation.
+Furthermore, we designed a module to enhance our privacy notion by protecting the actual timestamps of the {\thethings}.
 We differ the experimental evaluation of our methodology to Chapter~\ref{ch:eval} we experiment with real and synthetic data sets to demonstrate the applicability of the {\thething} privacy models by themselves (Section~\ref{sec:eval-lmdk-sel}) and in combination with the {\thething} selection component (Section~\ref{sec:eval-lmdk}).
 
 %Our experiments on real and synthetic data sets validate our proposal.