From 6261108d936c0438212414eb5b9102c6025a8218 Mon Sep 17 00:00:00 2001 From: Manos Katsomallos Date: Fri, 16 Jul 2021 02:28:41 +0200 Subject: [PATCH] abstract: First commit --- abstract.tex | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/abstract.tex b/abstract.tex index 3d0f4bd..f09c1cb 100644 --- a/abstract.tex +++ b/abstract.tex @@ -1,7 +1,26 @@ \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