An Incentive Approach in Mobile Crowdsensing for Perceptual User

The privacy protection of perceptual user and their enthusiasm improvement for participating in perceptual tasks are two important problems in MCS (Mobile Crowdsensing) network. A mechanism of local differential privacy protection of attribute correlation can generate perceptual results with higher precision of attribute correlation and protect perceptual users’ privacy data. A flow compensation incentive model for perceptual users’ privacy data protection based on opportunity cooperation transmission can reduce the flow compensation expenditure of MCS and improve perceptual users’ enthusiasm. Experiments show that our approach improves the perceptual result precision, reduces MCS overhead, and reduces flow compensation cost compared with the related approaches.