Incentive Mechanism for Continuous Crowd Sensing Based Symmetric Encryption and Double Truth Discovery - XU Miaomiao, CHEN Zhenping (Jisuanji kexue, 2023)
Aimed at the problems in continuous crowd sensing,such as the increased privacy requirements,the unreliable perception data collected and the low enthusiasm of users to participate,this paper proposes an incentive mechanism based on symmetric encryption and double-layer truth discovery(SDIM).First,the symmetric encryption algorithm is used to protect the privacy of the perceived data.When the privacy requirements are high and the number of perceptions is large,the computing overhead and the time of data encryption and reward computing will be greatly reduced.Second,based on a double-layer truth discovery model,an incentive mechanism supporting data reliability evaluation is proposed.The purpose is to simultaneously realize the real time reward of continuous crowd sensing,and improve the fairness of reward when the participants have malicious behavior.Finally,the dual privacy analysis of the proposed method is illustrated.The simulation results show that the proposed method can effectively calculate the true value and the reward according to the data reliability.Notably,it is obviously superior to the comparative model in the time of data encryption and reward computing,and can calculate the reward more fairly when the participants have malicious behavior.