Research On Privacy Protection Algorithm In Crowd Sensing Network

With the rapid development of science and technology,various smart devices integrate a wide variety of powerful sensors,making crowd sensing networks more and more widely used.Because crowd sensing networks are "people-centered" networks,the collected sensing data hides a large amount of sensitive information from the users.Once the information is leaked,it will seriously endanger the privacy and safety of the users,and it will also be harmful to the further development of the crowd sensing networks.Therefore,how to protect the user's privacy in the process of collecting the sensing data and how to ensure the security and integrity of data in the process of fusion and transmission have become the focus of research in recent years.This article carries out further research on the basis of the predecessors,and the main contents are as follows:The existing privacy protection strategies in Crowd Sensing Networks used the same privacy policies for all locations which led to the problems that some locations were overprotected,others were not adequately protected and the sensing data was less accurate.In order to solve this problem,a location privacy protection algorithm was proposed to meet the users' personalized privacy and security requirements.First,users' access duration,frequency and regularity at different locations were mined according to the user's historical movement trajectory,which were used to predict the social attributes of the locations to the users.Then,the location's social attributes and natural attributes were combined to predict user-location sensitivity levels.Finally,considering the different privacy security requirements of users in different locations,a dynamic privacy decision scheme was set up,and users with less sensitivity at each location were selected to participate in sensing tasks.The simulation results show that the proposed algorithm can improve the privacy protection level and the accuracy of the sensing data.Sensing data has a great risk of leakage in the process of fusion and transmission.Most existing privacy protection methods focus on the privacy of data and ignore the integrity of data.Privacy protection schemes that take into account data privacy and integrity have a large amount of communications,and the protection strength needs to be improved,which seriously affects the application of crowd sensing networks in practice.To solve this problem,this paper proposes a data fusion privacy protection algorithm based on distributed compressive sensing and hash function.The distributed compressivesensing method is used to sparsely observe the sensed data and remove the redundant data.Then use the one-way hash function to obtain the hash value of the observation data of the sensing data and fill it with the unconstrained camouflage data into the real sensory data to achieve the purpose of concealing the true sensor data.After the final sink node extracts the faked data,the hash value is obtained again to verify the integrity of the sensed data.The simulation results show that the algorithm takes into account the privacy and integrity of the data,and greatly reduces the communication overhead in the network.It has strong applicability and extensibility in practical applications.