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
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    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
  • English
Classification
Computer science
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/306291
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