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	Location Privacy-Preserving Task Allocation for Mobile Crowdsensing with Differential Geo-Obfuscation
	Wang, Leye1; Yang, Dingqi2; Han, Xiao3; Wang, Tianben4; Zhang, Daqing5; Ma, Xiaojuan1
	2017
关键词	Crowdsensing task allocation differential location privacy
DOI	10.1145/3038912.3052696
页码	627-636
英文摘要	In traditional mobile crowdsensing applications, organizers need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of their locations raises privacy concerns. Especially for those who are not eventually selected for any task, their location privacy is sacrificed in vain. Hence, in this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. Specifically, we make participants obfuscate their reported locations under the guarantee of differential privacy, which can provide privacy protection regardless of adversaries' prior knowledge and without the involvement of any third-part entity. In order to achieve optimal task allocation with such differential geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraint of differential privacy. Evaluation results on both simulation and real-world user mobility traces show the effectiveness of our proposed framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art differential geo-obfuscation mechanism, by achieving 45% less average travel distance on the real-world data.
会议录出版者	ASSOC COMPUTING MACHINERY
会议录出版地	1515 BROADWAY, NEW YORK, NY 10036-9998 USA
语种	英语
WOS研究方向	Computer Science
WOS记录号	WOS:000461544900067
内容类型	会议论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/3008]
专题	上海财经大学
作者单位	1.Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China; 2.Univ Fribourg, Fribourg, Switzerland; 3.Shanghai Univ Finance & Econ, Shanghai, Peoples R China; 4.Northwestern Polytech Univ, Xian, Shaanxi, Peoples R China; 5.Peking Univ, Key Lab High Confidence Software Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Leye,Yang, Dingqi,Han, Xiao,et al. Location Privacy-Preserving Task Allocation for Mobile Crowdsensing with Differential Geo-Obfuscation[C]. 见:.

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