abstract: First commit

abstract.tex

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 \chapter{Abstract}
 \label{ch:abs}
 
-This is the abstract.
+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 work, 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 two privacy models that guarantee {\thething} privacy and validate our proposal on real and synthetic data sets.
+
 
 \paragraph{Keywords:}
-keyword1, keyword2, keyword3.
+information privacy, continuous data publishing, crowdsensing, privacy-preserving data processing