Reference : The Privacy Exposure Problem in Mobile Location-Based Services
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/39020
The Privacy Exposure Problem in Mobile Location-Based Services
English
Wu, Fang-Jing mailto [Institute for Infocomm Research, A*STAR - Singapore]
Brust, Matthias R. mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Chen, Yiwen mailto [University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Center for Research in Economic Analysis (CREA)]
Luo, Tie mailto [Institute for Infocomm Research, A*STAR - Singapore]
2016
2016 IEEE Global Communications Conference (GLOBECOM)
Yes
IEEE Global Communications Conference (IEEE GLOBECOM)
2016
[en] mobile communication ; security ; data privacy
[en] Mobile location-based services (LBSs) empowered by mobile crowdsourcing provide users with context- aware intelligent services based on user locations. As smartphones are capable of collecting and disseminating massive user location-embedded sensing information, privacy preservation for mobile users has become a crucial issue. This paper proposes a metric called privacy exposure to quantify the notion of privacy, which is subjective and qualitative in nature, in order to support mobile LBSs to evaluate the effectiveness of privacy-preserving solutions. This metric incorporates activity coverage and activity uniformity to address two primary privacy threats, namely activity hotspot disclosure and activity transition disclosure. In addition, we propose an algorithm to minimize privacy exposure for mobile LBSs. We evaluate the proposed metric and the privacy-preserving sensing algorithm via extensive simulations. Moreover, we have also implemented the algorithm in an Android-based mobile system and conducted real-world experiments. Both our simulations and experimental results demonstrate that (1) the proposed metric can properly quantify the privacy exposure level of human activities in the spatial domain and (2) the proposed algorithm can effectively cloak users' activity hotspots and transitions at both high and low user-mobility levels.
http://hdl.handle.net/10993/39020
10.1109/GLOCOM.2016.7842319
https://ieeexplore.ieee.org/abstract/document/7842319
1-7

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
1808.01010.pdfArXiv PDFAuthor postprint1.07 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.