標題: The Privacy Exposure Problem in Mobile Location-based Services
作者: Wu, Fang-Jing
Brust, Matthias R.
Chen, Yan-Ann
Luo, Tie
資訊工程學系
Department of Computer Science
關鍵字: Crowdsourcing;participatory sensing;cyber-physical systems;data analytics;location-based services;privacy protection;smart cities
公開日期: 1-Jan-2016
摘要: 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.
URI: http://hdl.handle.net/11536/146577
ISSN: 2334-0983
期刊: 2016 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
Appears in Collections:Conferences Paper