Research On Location Privacy-Preserving In Spatio-Temporal Crowdsourcing System

In recent years,with the rapid development of mobile Internet and smart mobile device technology,spatio-temporal crowdsourcing has become a research hotspot in mobile crowd sensing networks(MCSS).In particular,the rapid development of sharing economy model has integrated spatio-temporal crowdsourcing into the real application scenarios.Many spatio-temporal crowdsourcing platforms keep emerging,such as Uber,Didi,Meituan take-away,Ali crowdsourcing,etc.In the process of spatio-temporal crowdsourcing,spatio-temporal crowdsourcing users need to send data containing their own spatio-temporal information to the spatio-temporal crowdsourcing platform,so as to provide accurate services for users.However,the untruthful spatio-temporal crowdsourcing platform and malicious attackers inevitably bring great threats to the location privacy of spatio-temporal crowdsourcing users.Therefore,how to protect the location privacy of spatio-temporal crowdsourcing users is a key problem in spatio-temporal crowdsourcing research.However,with the increase of privacy protection,the quality of service of spatio-temporal crowdsourcing users will be affected.In view of the above problems,this paper analyzes the advantages and disadvantages of the existing location privacy protection technologies in spatio-temporal crowdsourcing.Based on the characteristics of spatio-temporal crowdsourcing in time and space,this paper proposes a spatio-temporal crowdsourcing privacy protection model,and conducts simulation experiments to verify its feasibility and effectiveness.The main research contents of this paper are shown as follows:(1)For the problems that k-anonymous location privacy protection technology can not effectively prevent continuous attacking,and under the differential privacy protection technology,attackers still has larger probability to infer the spatio-temporal crowdsourcing users'true locations through applying differential attack algorithm,based on the k-anonymous privacy protection technology and differential privacy protection technology,this paper proposes a privacy-preserving method of spatio-temporal crowdsourcing system to protect the real-time location data of users when they perform a crowd task.It can improve the level of privacy-preserving for spatio-temporal crowdsourcing users.Through comparing the the probabilities that attackers identify the real location of spatio-temporal crowdsourcing users and the expected distance error of attackers,two kinds of simulation experiments are conducted to verify the feasibility and effectiveness of the proposed privacy-preserving method.(2)According to the problems that the data transmission efficiency is low in the process of data transmission and linear data aggregation and the spatio-temporal crowdsourcing users' waiting time is too long,this paper proposes a distributed dynamic data aggregation method to improve the data transmission efficiency and reduce the waiting time of spatio-temporal crowdsourcing.(3)When the privacy-preserving intensity of spatio-temporal crowdsourcing users is low,the location privacy security cannot be effectively protected.However,when the privacy-preserving intensity of spatio-temporal crowdsourcing users is high,the service quality will be reduced and the sensed data quality will be affected.For the problem,this paper proposes a Starkberg game-based method to solve the conflict between the privacy-preserving intensity and the service quality.Through simulation experiments,the effectiveness of the algorithm is verified.