A high percentage of these data carry information of user activities and other personal details, and thus their manipulation and sharing raise concerns about the privacy of the individuals involved.
To enable the secure---from the user privacy perspective---data sharing, researchers have already proposed various seminal techniques for the protection of user privacy while accounting for data utility and quality.
However, the continuous fashion in which data are generated nowadays, and the high availability of external sources of information, pose more threats and add extra challenges to the problem, due to inevitable correlations that arise.
\kat{I still do not see the motivation/challenges for landmark privacy. Saying about configurability is not enough.. We should see sth about overperturbing the data when not necessary by the context..}
It is therefore essential to design solutions that not only guarantee a balance between user privacy protection and data utility, but also provide configurability and consider the preferences of the users.
Initially, we investigate the literature regarding data privacy in continuous data publishing, and report on the proposed solutions, with a special focus on solutions concerning location or geo-referenced data.
We provide an insight into time-related properties of the algorithms, e.g.,~if they work on finite or infinite data, or if they take into consideration any underlying type of data correlation.
Thereafter, we proceed to propose a novel type of data privacy, called \emph{{\thething} privacy}.
We observe that in continuous data publishing, events are not equally significant in terms of privacy, and hence they should affect the privacy-preserving processing differently.
The existing differential privacy protection levels protect either a single timestamp, or all the data per user or per window in the time series; however, considering all timestamps as equally significant.
The novel notion that we propose, {\thething} privacy, is based on differential privacy and allocates the available privacy budget while taking into account significant events (\emph{\thethings}) in the time series.
We design three privacy schemes that guarantee {\thething} privacy and further extend them by providing more robust privacy protection to the {\thething} set with the design of a dummy {\thething} selection module.
We assess the impact on data utility for several possible {\thething} distributions, with emphasis on situations under the presence of temporal correlation.
Overall, the results of the experimental evaluation and comparative analysis of {\thething} privacy validate its applicability to several use case scenarios and showcase the improvement, in terms of data utility, over the existing privacy protection levels.
Particularly, the dummy {\thething} selection module achieves better {\thething} protection, provoking only a reasonable data utility decline. \kat{what is reasonable?}
In terms of temporal correlation, we observe that under moderate and strong correlation, greater average regular–{\thething} event distance causes greater overall privacy loss.
