The Privacy Exposure Problem in Mobile Location-Based Services
Abstract
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.
Recommended Citation
F. Wu et al., "The Privacy Exposure Problem in Mobile Location-Based Services," Proceedings of the 59th IEEE Global Communications Conference (2016, Washington, DC), Institute of Electrical and Electronics Engineers (IEEE), Dec 2016.
The definitive version is available at https://doi.org/10.1109/GLOCOM.2016.7842319
Meeting Name
59th IEEE Global Communications Conference, GLOBECOM 2016 (2016: Dec. 4-8, Washington, DC)
Department(s)
Computer Science
Keywords and Phrases
Data privacy; Location; Mobile telecommunication systems; Telecommunication services, Extensive simulations; Intelligent Services; Mobile crowdsourcing; Privacy preservation; Privacy preserving; Privacy preserving solutions; Real world experiment; Sensing algorithms, Location based services
International Standard Book Number (ISBN)
978-150901328-9
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Dec 2016
