\section{Contribution}
\label{sec:lmdk-contrib}

In this chapter, we formally define a novel privacy notion that we call \emph{{\thething} privacy}.
We apply this privacy notion to time series consisting of \emph{{\thethings}} and regular events, and we design and implement three {\thething} privacy mechanisms.
We further study {\thething} privacy under temporal correlation that is inherent in time series publishing.
Finally, we evaluate {\thething} privacy with real and synthetic data sets, in settings with or without temporal correlation, showcasing the validity of our model.
