Spatial Crowdsourcing Quality Control Model Based on K-Anonymity Location Privacy Protection and ELM Spammer Detection
Joint Authors
Cheng, Zhaolin
Zeng, Mengjia
Huang, Xu
Zheng, Bo
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-04
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Telecommunications Engineering
Abstract EN
The spatial crowdsourcing task places workers at a risk of privacy leakage.
If positional information is not required to submit, it will result in an increased error rate and number of spammers, which together affects the quality of spatial crowdsourcing.
In this paper, a spatial crowdsourcing quality control model is proposed, called SCQCM.
In the model, the spatial k-anonymity algorithm is used to protect the position privacy of the general spatial crowdsourcing workers.
Next, an ELM (extreme learning machine) algorithm is used to detect spammers, while an EM (expectation maximization) algorithm is used to estimate the error rate.
Finally, different parameters are selected, and the efficiency of the model is simulated.
The results showed that the spatial crowdsourcing model proposed in this paper guaranteed the quality of crowdsourcing projects on the premise of protecting the privacy of workers.
American Psychological Association (APA)
Zeng, Mengjia& Cheng, Zhaolin& Huang, Xu& Zheng, Bo. 2019. Spatial Crowdsourcing Quality Control Model Based on K-Anonymity Location Privacy Protection and ELM Spammer Detection. Mobile Information Systems،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1193726
Modern Language Association (MLA)
Zeng, Mengjia…[et al.]. Spatial Crowdsourcing Quality Control Model Based on K-Anonymity Location Privacy Protection and ELM Spammer Detection. Mobile Information Systems No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1193726
American Medical Association (AMA)
Zeng, Mengjia& Cheng, Zhaolin& Huang, Xu& Zheng, Bo. Spatial Crowdsourcing Quality Control Model Based on K-Anonymity Location Privacy Protection and ELM Spammer Detection. Mobile Information Systems. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1193726
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193726