Sensors, portable devices, and location-based services, generate massive amounts of geo-tagged, and/or location- and user-related data on a daily basis.
A high percentage of these data carry information of user 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 user privacy perspective---data sharing, researchers have already proposed various seminal techniques for the protection of user 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.
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It is therefore essential to design solutions that not only guarantee privacy protection but also provide configurability and account the preferences of the users.
In this thesis, 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.
As a matter of fact, a wealth of algorithms has been proposed for privacy-preserving data publishing, either for microdata or statistical data.
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 dependence.
Having discussed the literature around continuous data publishing, we continue to propose a novel type of data privacy, called \emph{{\thething} privacy}.
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 differently.
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.
The novel scheme that we propose, {\thething} privacy, is based on differential privacy, but also takes into account significant events (\emph{\thethings}) in the time series and allocates the available privacy budget accordingly.
We design three privacy schemes that guarantee {\thething} privacy and further extend them in order to provide more robust privacy protection to the {\thething} set.
We evaluate our proposal on real and synthetic data sets and assess the impact on data utility with emphasis on situations under the presence of temporal correlation.
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The results of the experimental evaluation and comparative analysis of {\thething} privacy validate its applicability to several use case scenarios with and without the presence of temporal correlation.
