Sensors, portable devices, and location-based services, generate massive amounts of geo-tagged, and/or location- and user-related data on a daily basis.
The manipulation of such data is useful in numerous application domains, e.g.,~healthcare, intelligent buildings, and traffic monitoring, to name a few.
A high percentage of these data carry information of users' activities and other personal details, and thus their manipulation and sharing arise concerns about the privacy of the individuals involved.
To enable the secure---from the users' privacy perspective---data sharing, researchers have already proposed various seminal techniques for the protection of users' privacy.
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.
% Survey
In the first part, we visit the works done on data privacy for continuous data publishing, and report on the proposed solutions, with a special focus on solutions concerning location or geo-referenced data.
As a matter of fact, a wealth of algorithms have been proposed for privacy-preserving data publishing, either for microdata or statistical data.
In this context, this part seeks to offer a guide that would allow its users to choose the proper algorithm(s) for their specific use case accordingly.
We provide an insight into time-related properties of the algorithms, e.g.,~if they work on infinite, real-time data, or if they take into consideration existing data dependencies.
% Landmarks
In the second part, we argue that in continuous data publishing, events are not equally significant in terms of privacy, and hence they should affect the privacy-preserving processing.
Differential privacy is a well-established paradigm in privacy-preserving time series publishing.
Different schemes exist, protecting either a single timestamp, or all the data per user or per window in the time series, considering however all timestamps as equally significant.
In this part, 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 design three privacy models that guarantee {\thething} privacy and validate our proposal on real and synthetic data sets.
