Preprint

Geographic differential privacy for mobile crowd coverage maximization

  • Wang, Leye The Hong Kong University of Science and Technology, China
  • Qin, Gehua Shanghai Jiao Tong University, China
  • Yang, Dingqi University of Fribourg, Switzerland
  • Han, Xiao Shanghai University of Finance and Economics, China
  • Ma, Xiaojuan The Hong Kong University of Science and Technology, China
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    2018
English For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In this paper, we propose a method to maximize mobile crowd's future location coverage under a guaranteed location privacy protection scheme. In our approach, users only need to upload one of their frequently visited locations, and more importantly, the uploaded location is obfuscated using a geographic differential privacy policy. We propose both analytic and practical solutions to this problem. Experiments on real user mobility datasets show that our method significantly outperforms the state-of-the-art geographic differential privacy methods by achieving a higher coverage under the same level of privacy protection.
Faculty
Faculté des sciences et de médecine
Department
Département d'Informatique
Language
  • English
Classification
Computer science
License
License undefined
Identifiers
  • RERO DOC 308933
Persistent URL
https://folia.unifr.ch/unifr/documents/306268
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