33 lines
3.4 KiB
TeX
33 lines
3.4 KiB
TeX
\chapter{Abstract}
|
|
\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.
|
|
The manipulation of such data is useful in numerous application domains, e.g.,~healthcare, intelligent buildings, and traffic monitoring.
|
|
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.
|
|
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 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.
|
|
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 infinite, real-time data, or if they take into consideration existing data dependence.
|
|
|
|
% Landmarks
|
|
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.
|
|
% \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.
|
|
|
|
|
|
\paragraph{Keywords:}
|
|
information privacy, continuous data publishing, crowdsensing, privacy-preserving data processing
|