A Secure and Efficient Task Matching Scheme for Spatial Crowdsourcing

Abstract

The sharing economy has greatly promoted the rapid development and application of spatial crowdsourcing. Although privacy-preserving spatial task matching as an indispensable part has been extensively explored, existing schemes cannot be deployed into the practical environment due to drawbacks in the one-side location protection, the matching efficiency, and the dynamic updates. In this study, we propose a novel Secure and Efficient Spatial Task Matching framework (SESTM) with utilizing multiuser searchable encryption and secure index technique, which enables to preserve the location privacy of requesters and workers while achieving efficient task allocation and good user scalability. Specifically, requesters firstly transform and encrypt their task locations before being outsourced, and we secondly design a secure and dynamic tree-based index SD-Tree for SC-server to merge these uploaded encrypted data without knowing their underlying content. Finally, SESTM provides efficient task matching services for multiple workers based on encrypted queries. Furthermore, SD-Tree also provides fast delete and insert operations under logarithmic time to reduce the dynamic update overhead for real SC services. Extensive theoretical analysis and performance evaluation demonstrate the practicality of our method.

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

“…Other studies have focused on the optimization of spatial task assignment. For instance, Zhou et al [26] proposed a secure and efficient spatial task matching framework that utilizes multi-user searchable encryption and secure index technique. Abdullah et al [27] introduced the use of Bayesian Network in modelling and selecting optimal workers and used k-medoids partitioning technique for tasks clustering and scheduling.…”