Location privacy-preserving task allocation for mobile crowdsensing with differential geo-obfuscation

Wang, Leye; Yang, Dingqi; Han, Xiao; Wang, Tianben; Zhang, Daqing; Ma, Xiaojuan

doi:10.1145/3038912.3052696

Back

Conference paper (in proceedings)

Location privacy-preserving task allocation for mobile crowdsensing with differential geo-obfuscation

Wang, Leye Hong Kong University of Science and Technology, Hong Kong, Hong Kong
Yang, Dingqi University of Fribourg, Fribourg, Switzerland
Han, Xiao Shanghai University of Finance and Economics, Shanghai, China
Wang, Tianben Northwestern Polytechnical University, Xi'an, China
Zhang, Daqing Peking University, Beijing, China
Ma, Xiaojuan Hong Kong University of Science and Technology, Hong Kong, Hong Kong

2017

Published in:

Proceedings of the 26th International Conference on World Wide Web. - 2017, p. 627–636

English 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.

Faculty

Faculté des sciences et de médecine

Department

Département d'Informatique

Language