Mobile Crowdsourcing Task Allocation with Differential-and-Distortion Geo-Obfuscation

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 trustful 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 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 show the effectiveness of our framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art geo-obfuscation mechanism, by achieving up to 47% less average travel distance under the same level of privacy protection.