In this chapter, we propose a novel configurable privacy scheme, \emph{{\thething} privacy}, which takes into account significant events (\emph{\thethings}) in the time series and allocates the available privacy budget accordingly.
We propose three privacy models that guarantee {\thething} privacy and validate our proposal on real and synthetic data sets.
In this chapter, we propose a novel configurable privacy scheme, \emph{\thething} privacy, which takes into account significant events (\emph{\thethings}) in the time series and allocates the available privacy budget accordingly.
We propose two privacy models that guarantee {\thething} privacy.
To further enhance our privacy method, and protect the landmarks position in the time series, we propose techniques to perturb the initial landmarks set (Section~\ref{sec:theotherthing}).
% and validate our proposal on real and synthetic data sets. \kat{this will go in the experiments section}