Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
7-2021
Abstract
Spatial crowdsourcing has stimulated various new applications such as taxi calling and food delivery. A key enabler for these spatial crowdsourcing based applications is to plan routes for crowd workers to execute tasks given diverse requirements of workers and the spatial crowdsourcing platform. Despite extensive studies on task planning in spatial crowdsourcing, few have accounted for the location privacy of tasks, which may be misused by an untrustworthy platform. In this paper, we explore efficient task planning for workers while protecting the locations of tasks. Specifically, we define the Privacy-Preserving Task Planning (PPTP) problem, which aims at both total revenue maximization of the platform and differential privacy of task locations. We first apply the Laplacian mechanism to protect location privacy, and analyze its impact on the total revenue. Then we propose an effective and efficient task planning algorithm for the PPTP problem. Extensive experiments on both synthetic and real datasets validate the advantages of our algorithm in terms of total revenue and time cost.
Keywords
Spatial Crowdsourcing, Privacy Preserving, Task Planning
Discipline
Databases and Information Systems | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of 22nd IEEE International Conference on Mobile Data Management (MDM), Toronto, Canada, 2021 June 15-18
First Page
1
Last Page
10
Identifier
10.1109/MDM52706.2021.00015
Publisher
IEEE
City or Country
Toronto, ON, Canada
Citation
TAO, Qian; TONG, Yongxin; LI, Shuyuan; ZENG, Yuxiang; ZHOU, Zimu; and XU, Ke.
A differentially private task planning framework for spatial crowdsourcing. (2021). Proceedings of 22nd IEEE International Conference on Mobile Data Management (MDM), Toronto, Canada, 2021 June 15-18. 1-10.
Available at: https://ink.library.smu.edu.sg/sis_research/6709
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.