Toward location privacy protection in Spatial crowdsourcing. (March 2019)

• Record Type: Journal Article
• Authors: Ye, Hang; Han, Kai; Xu, Chaoting; Xu, Jingxin; Gui, Fei
• Is Part Of: International journal of distributed sensor networks. Volume 15:Number 3(2019)
• Publication Date: 2019-03
• Subjects: Spatial crowdsourcing -- spatio-temporal -- privacy-preserving; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147719830568
• Languages: English
• ISSNs: 1550-1329

Abstract

Spatial crowdsourcing is an emerging outsourcing platform that allocates spatio-temporal tasks to a set of workers. Then, the worker moves to the specified locations to perform the tasks. However, it usually demands workers to upload their location information to the spatial crowdsourcing server, which unavoidably attracts attention to the privacy-preserving of the workers' locations. In this article, we propose a novel framework that can protect the location privacy of the workers and the requesters when assigning tasks to workers. Our scheme is based on mathematical transformation to the location while providing privacy protection to workers and requesters. Moreover, to further preserve the relative location between workers, we generate a certain amount of noise to interfere the spatial crowdsourcing server. Experimental results on real-world data sets show the effectiveness and efficiency of our proposed framework.