Spatial crowdsourcing quality control model based on k-anonymity location privacy protection and ELM spammer detection

Abstract

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.