群智感知网络个性化位置隐私保护算法 postprint

Abstract: The existing privacy protection strategies in crowd sensing networks used the same privacy policies for all locations which overprotected led to the problems that some locations, others were not adequately protected and the sensing data was less accurate. In order to solve this problem, this paper proposed a location privacy protection algorithm to meet the users’ personalized privacy and security requirements. First, it mined users’ access duration, frequency and regularity at different locations according to the user's historical movement trajectory, which used to predict the social attributes of the locations to the users. Then, it combined the location’s social attributes and natural attributes to predict user-location sensitivity levels. Finally, considering the different privacy security requirements of users in different locations, it set a dynamic privacy decision scheme. Users with less sensitivity at each location were selected to participate in sensing tasks to ensure that users, in the safe privacy context, could contribute the accurate data with a higher level of spatiotemporal correlation. The simulation results show that the algorithm can improve the privacy protection level and the accuracy of the sensing data.