Research On Privacy Protection Of User Identity And Sensing Data In Mobile Crowdsensing

The development of the Internet of Things has risen to the national strategy.As a new sensing mode of the Internet of Things,mobile crowdsensing has developed rapidly through a large number of mobile intelligent sensing terminals with sensing ability to cooperate to complete a certain intelligent pan in-depth social sensing task,providing a new information service mode for pan in-depth social sensing.However,in the process of sensing task execution,sensing users are faced with a variety of risks of user identity and data privacy disclosure,which seriously endangers the security of sensing user identity information and data.Therefore,how to protect the identity and data privacy of sensing users in the process of task execution is one of the hot research topics of current researchers.In this dissertation,the main innovative work is as follows:In mobile crowdsensing,attackers can reconstruct the social circle among sensing users,who use the social association information among sensing users and the correlation between the sensing user’s identity and sensing data to further attack a social alliance.In order to tackle this issue,a fog-aided identity privacy protection scheme is proposed.Firstly,two fog nodes are introduced which located at the edge of the sensing terminal.The one is task allocation center(TC)for handling the reasonable allocation of sensing tasks,and the other is data center(DC)for calculating sensing data.Furthermore,differential privacy is employed for preventing attackers from acquiring the specific social association weight of sensing users.Finally,in order to prevent attackers from obtaining the sensing users’ identity information and sensing data at the same time,sensing users use different blind identities to communicate with TC and DC.Security analysis indicates that the proposed scheme can ensure the security of identity privacy information of sensing users in the process of completing sensing tasks.Experimental results show that the proposed scheme can protect the social association information between sensing users,and has a low delay.Aiming at the balance between user personalized privacy protection and task data practicability in mobile crowdsensing,it proposes a lightweight privacy protection(Light Privacy)scheme based on the attribute preferences between users and tasks with analytic hierarchy process(AHP),bloom filter,binary confusion vector inner product protocol and differential privacy.Firstly,the fog nodes are introduced to divide the sub-tasks of sensing tasks,and the optimal sub-task set is selected and published through AHP.The fog nodes construct task bloom filter according to task attribute requirement preference.The sensing users construct user bloom filter based on intention attribute preference,and filter target users by calculating the binary confusion vector inner product of two bloom filters.Furthermore,the Light Privacy scheme perceives sensing users to localize sensitive data that needs to be disturbed according to privacy budget distributed equally by fog nodes.Finally,the fog nodes evaluate the quality of sensing data and define task contribution of sensing users in combination with the binary confusion vector inner product,so as to effectively prevent malicious users from submitting false data or adding large data perturbations.Security analysis indicates that the Light Privacy scheme is still security under the condition that fog nodes are semi-trusted.Experimental results show that the Light Privacy scheme is practical and the computational efficiency is significantly improved compared with the related representative schemes.