| With the increasing popularity of smart mobile devices,mobile crowd sensing has been widely studied and applied.Due to the characteristics of low deployment cost,strong mobility and large space coverage,mobile crowd sensing is considered to be the most promising data collection method.However,the user’s non-professional nature and the complexity of the external environment make the collected sensing data often unreliable,and even users maliciously transmit forged data.Therefore,only by filtering out the incorrect sensing data and identifying the real data information can the quality of the data obtained by the mobile crowd sensing be guaranteed.In addition,the senisng data uploaded by users may contain sensitive information,and attackers may use this sensitive information to carry out malicious attacks,which reduces the enthusiasm of users to participate in the system.Therefore,in mobile crowd sensing,it is very important to identify true and reliable data information while protecting data privacy.Based on this,the main research work and results of this article are as follows:(1)Aiming at the problem of user location privacy protection in mobile crowd sensing,a novel data fusion truth discovery algorithm for location privacy protection,PPDATD,is proposed,which updates user weights and evaluates the true value of the object through secure iterations to obtain the object’s true location.PPDATD first divides users into several clusters.Nodes in the cluster use secret random numbers to prevent other nodes from obtaining their location data,ensuring location privacy.Secondly,the cluster head node obtains the location data fusion result in the cluster using the security summation protocol and encrypts it and uploads it to the server,and then the node completes the weight update according to the aggregation result broadcast by the server.Since the server obtains aggregated data,it is impossible to deduce the location data and weights of individual users.Finally,the truth evaluation is performed in the same way.In addition,PPDATD provides corresponding processing mechanisms for users’ failure exit and dynamic joining,which enhances the robustness of the system.Experimental results show that PPDATD has the characteristics of high accuracy,low communication,and high security.(2)Aiming at the problem of user data privacy protection in ’s,an efficient slicebased truth discovery privacy protection protocol EPPTDS in mobile group intelligence perception is proposed,which updates user weights and evaluation object truth values through secure iterations.So as to get the real data of the object.EPPTDS first divides users into several clusters according to their location and number.The nodes in each cluster divide the sensing data into two parts according to the extended Euclidean algorithm,and one part of the data is sent to any other node in the cluster.Within a certain period of time,each node in the cluster will receive a slice of any other node.Secondly,the node mixes the received slice data with the remaining slice data and sends them to the cluster head node to ensure the privacy of the data of a single node.Finally,the cluster head node merges the slice data of all nodes in the cluster and uploads it to the server,and the node completes the weight update according to the aggregation result broadcast by the server,and the server cannot obtain the location data and weight of a single user.Finally,the truth evaluation is performed in the same way.In addition,in order to further reduce the user’s communication overhead and enhance the practicability of the system,this paper proposes an improved and efficient mobile group intelligence perception based on the slice-based truth discovery privacy protection protocol M-EPPTDS on the basis of EPPTDS.The slicing forwarding requirement reduces the amount of communication while ensuring data privacy.Experimental results show that EPPTDS can identify true and reliable data information while protecting data privacy. |
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