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.
