A Dual Privacy Preserving Algorithm in Spatial Crowdsourcing
Joint Authors
Jia, Xiaofan
Wang, Shengxiang
Sang, Qianqian
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-27
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Telecommunications Engineering
Abstract EN
Spatial crowdsourcing assigns location-related tasks to a group of workers (people equipped with smart devices and willing to complete the tasks), who complete the tasks according to their scope of work.
Since space crowdsourcing usually requires workers’ location information to be uploaded to the crowdsourcing server, it inevitably causes the privacy disclosure of workers.
At the same time, it is difficult to allocate tasks effectively in space crowdsourcing.
Therefore, in order to improve the task allocation efficiency of spatial crowdsourcing in the case of large task quantity and improve the degree of privacy protection for workers, a new algorithm is proposed in this paper, which can improve the efficiency of task allocation by disturbing the location of workers and task requesters through k-anonymity.
Experiments show that the algorithm can improve the efficiency of task allocation effectively, reduce the task waiting time, improve the privacy of workers and task location, and improve the efficiency of space crowdsourcing service when facing a large quantity of tasks.
American Psychological Association (APA)
Wang, Shengxiang& Jia, Xiaofan& Sang, Qianqian. 2020. A Dual Privacy Preserving Algorithm in Spatial Crowdsourcing. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1192335
Modern Language Association (MLA)
Wang, Shengxiang…[et al.]. A Dual Privacy Preserving Algorithm in Spatial Crowdsourcing. Mobile Information Systems No. 2020 (2020), pp.1-6.
https://search.emarefa.net/detail/BIM-1192335
American Medical Association (AMA)
Wang, Shengxiang& Jia, Xiaofan& Sang, Qianqian. A Dual Privacy Preserving Algorithm in Spatial Crowdsourcing. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1192335
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192335