abstract: Rewrote according to sections

text/abstract.tex

@@ -2,30 +2,38 @@
 \label{ch:abs}
 % \kat{Il faut aussi en francais :) }
 % \mk{D'accord :( }
-Sensors, portable devices, and location-based services, generate massive amounts of geo-tagged, and/or location- and user-related data on a daily basis.
+Sensors, portable devices, and crowdsensing applications
-The manipulation of such data is useful in numerous application domains, e.g.,~healthcare, intelligent buildings, and traffic monitoring.
+% , e.g.,~trajectory monitoring, smart metering, contact tracing, etc., 
+generate massive amounts of user-related, and usually geo-tagged, data on a daily basis.
+The manipulation of such data is useful in numerous application domains including traffic monitoring, intelligent building, and healthcare.
 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.
+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.
 % \kat{Mention here the extra challenges posed by the specific problem that you address :  the Landmark privacy}
-It is therefore essential to design solutions that not only guarantee privacy protection but also provide configurability and account for the preferences of the users.
+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.
 
 % Survey
-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.
+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.
 As a matter of fact, a wealth of algorithms has been proposed for privacy-preserving data publishing, either for microdata or statistical data.
 In this context, we seek to offer a guide that would allow readers 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 finite or infinite data, or if they take into consideration any underlying data dependence.
+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.
 
 % Landmarks
-Having discussed the literature around continuous data publishing, we proceed to propose a novel type of data privacy, called \emph{{\thething} privacy}. 
+Thereafter, we proceed 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.
+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.
 Differential privacy is a well-established paradigm in privacy-preserving time series publishing. 
-The existing differential privacy schemes 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 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 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.
+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 in order to provide more robust privacy protection to the {\thething} set.
+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 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.
+
+% Evaluation
+Finally, we evaluate the {\thething} privacy schemes and dummy {\thething} selection module, that we proposed, on real and synthetic data sets.
+We assess the impact on data utility for several possible {\thething} distributions, with emphasis on situations under the presence of temporal correlation.
 % \kat{add selection, and a small comment on the conclusions driven by the experiments.}
-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.
+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 introduces a reasonable data utility decline to all of the {\thething} privacy schemes.
+In terms of temporal correlation, we observe that under moderate and strong correlation, greater average regular–{\thething} event distance causes greater overall privacy loss.
+
 
 
 \paragraph{Keywords:}