Visible to the public A Differentially Private Task Planning Framework for Spatial Crowdsourcing

TitleA Differentially Private Task Planning Framework for Spatial Crowdsourcing
Publication TypeConference Paper
Year of Publication2021
AuthorsTao, Qian, Tong, Yongxin, Li, Shuyuan, Zeng, Yuxiang, Zhou, Zimu, Xu, Ke
Conference Name2021 22nd IEEE International Conference on Mobile Data Management (MDM)
Date Publishedjun
KeywordsConferences, crowdsourcing, Differential privacy, Human Behavior, Laplace equations, Metrics, outsourced database security, Planning, privacy, privacy preserving, pubcrawl, resilience, Resiliency, Scalability, spatial crowdsourcing, Spatial databases, Task Planning
AbstractSpatial 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.
DOI10.1109/MDM52706.2021.00015
Citation Keytao_differentially_2021