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Density-based location preservation for mobile crowdsensing with differential privacy

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journal contribution
posted on 2018-03-16, 00:00 authored by Mengmeng Yang, Tianqing Zhu, Yang Xiang, Wanlei Zhou
© 2013 IEEE. In recent years, the widespread prevalence of smart devices has created a new class of mobile Internet of Thing applications. Called mobile crowdsensing, these techniques use workers with mobile devices to collect data and send it to task requester for rewards. However, to ensure the optimal allocation of tasks, a centralized server needs to know the precise location of each user, but exposing the workers' exact locations raises privacy concerns. In this paper, we propose a data release mechanism for crowdsensing techniques that satisfies differential privacy, providing rigorous protection of worker locations. The partitioning method is based on worker density and considers non-uniform worker distribution. In addition, we propose a geocast region selection method for task assignment that effectively balances the task assignment success rate with worker travel distances and system overheads. Extensive experiments prove that the proposed method not only provides a strict privacy guarantee but also significantly improves performance.

History

Journal

IEEE Access

Volume

6

Pagination

14779 - 14789

Publisher

IEEE

Location

Piscataway, N.J.

eISSN

2169-3536

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2018, IEEE