Article in Proceedings INPROC-2016-11

BibliographyRiaz, Zohaib; Dürr, Frank; Rothermel, Kurt: On the Privacy of Frequently Visited User Locations.
In: Proceedings of the Seventeenth International Conference on Mobile Data Management: MDM'16; Porto, Portugal, June 13-16, 2016.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
pp. 1-10, english.
Porto, Portugal: IEEE Computer Society, June 13, 2016.
Article in Proceedings (Conference Paper).
CorporationIEEE International Conference on Mobile Data Management
CR-SchemaK.4.1 (Computers and Society Public Policy Issues)
C.2.4 (Distributed Systems)
KeywordsLocation Privacy, Location-based Applications, Semantic Locations, Visit-Frequency, Frequent locations, Geo-social networking, Location Servers, Non-trusted systems
Abstract

With the fast adoption of location-enabled devices, Location-based Applications (LBAs) have become widely popular. While LBAs enable highly useful concepts such as geo-social networking, their use also raises serious privacy concerns as it involves sharing of location data with non-trusted third parties. In this respect, we propose an approach that protects the frequently visited locations of users, e.g., a bar, against inferences from longterm monitoring of their location data. Such inferences equate a privacy leak as they reveal a user’s personal behavior and interests to possibly malicious non-trusted parties.

To this end, we first present a study of a dataset of location check-ins to show the existence of this threat among users of LBAs. We then propose our approach to protect visit-frequency of the users to different locations by distributing their location data among multiple third-party Location Servers. This distribution not only serves to avoid a single point of failure for privacy in our system, it also allows the users to control which LBA accesses what information about them. We also describe a number of possible attacks against our privacy approach and evaluate them on real-data from the check-ins dataset. Our results show that our approach can effectively hide the frequent locations while supporting good quality-of-service for the LBAs.

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Contactzohaib.riaz@ipvs.uni-stuttgart.de
Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems
Project(s)PriLoc
Entry dateApril 11, 2016
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