$k$-Anonymity Based Location Data Query Privacy Protection Method in Mobile Social Networks
Hongtao Li, Lingxia Gong, Bo Wang, Feng Guo, Jie Wang, Tao Zhang · 2020
With continuous development of mobile social network application services, researches on query privacy protection in location-based services has captured more and more interests from scholars. Aiming at the problem that privacy disclosure of location data and query content information of users in mobile social network applications, a privacy query method based on k-anonymity model is proposed in paper, which protects the user's location information on data processor side and location services provider respectively. On the privacy protection processor side, an anonymous frame construction algorithm based on k-anonymity location information is proposed, which randomly selects a point near the longitude and latitude grid as the center, and selects a straight line with the farthest distance between the center and the four vertices on the longitude and latitude grid as the initial radius R, and then constructs the user's location data anonymous frame according to the user-defined privacy configuration to protect the location information when the user queries. On the location service providers side, a location information query algorithm based on dynamic planning is proposed, which takes the center of location information anonymous frame of the query request user as the center, then obtains an area of range and uses the dynamic planning query algorithm to carry out the Top-k ranking to obtain the optimal answer tree, finally feeds back the query results of the Top-k answer tree to the privacy protection processor to protect the user's location query content. Performance analysis and experimental results show that our method can protect privacy and security of location data query with low loss of location data and acceptable efficiency and accuracy of location information query.