Journal
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
Volume 18, Issue 2, Pages 967-981Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TDSC.2019.2912886
Keywords
Task analysis; Privacy; Resource management; Distortion; Differential privacy; Crowdsourcing; Optimization; Mobile crowdsensing; differential location privacy; distortion location privacy; task allocation; travel distance
Categories
Funding
- NSFC [61572048, 71601106]
- State Language Commission of China [ZDI135-18]
- Hong Kong ITF [ITS/391/15FX]
- ERC [683253]
- European Research Council (ERC) [683253] Funding Source: European Research Council (ERC)
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This research proposes a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. Evaluation results verify the effectiveness of the framework, achieving up to 47 percent shorter average travel distance.
In mobile crowdsourcing, organizers usually need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of users' locations raises privacy concerns. In this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. More specifically, we make participants obfuscate their reported locations under the guarantee of two rigorous privacy-preserving schemes, differential and distortion privacy, without the need to involve any third-party trusted entity. In order to achieve optimal task allocation with the differential-and-distortion geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraints of differential and distortion privacy. Moreover, a worker may be willing to accept multiple tasks, and a task organizer may be concerned with multiple utility objectives such as task acceptance ratio in addition to travel distance. Against this background, we also extend our solution to the multi-task allocation and multi-objective optimization cases. Evaluation results on both simulation and real-world user mobility traces verify the effectiveness of our framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art geo-obfuscation mechanism, by achieving up to 47 percent shorter average travel distance on real-world data under the same level of privacy protection.
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