Research On Location Privacy Protection Based On Co-location Information

In recent years,with the development of space location technology,more and more users share information with location tags in mobile social networks.Unlike Location Based Service(LBS),mobile social networks not only exist offline interaction between users and positions,but also exist the online interaction between users and friends.At the same time,when users share their locations in mobile social networks,they often mark their friends who are together with them,namely co-location information.Therefore,user location information sharing will not only threaten their own location privacy,but also affect their friends' location privacy.For this reason,this paper proposes a location privacy protection method that takes account of online interaction between users and friends and takes account of co-location information.First,this paper analyzes the characteristics of mobile social network,discusses the location privacy protection methods and its mainstream architectures,and gives the existing calculation methods of the relation strength in the mobile social network.Then we design the location privacy protection system in mobile social network,give the system architecture,and illustrate the key components of the system.Then,this paper proposes a relation strength calculation method based on the online interaction between users and friends,and two location privacy preserving algorithms based on the relation strength and co-location information for location privacy protection,respectively,the LPPC algorithm based on users coordination and the CCTA algorithm based on co-location information hiding and time adjustment.Finally,we give the system implementation and external interfaces for the location privacy protection agent which is the key components of the system.We collect the twitter dataset to validate the effectiveness of the relation strength calculation method,In the meanwhile,we design a python program to get the point of interest data,then we use this data to validate the performance and the effectiveness of our location privacy protection algorithms.The result shows that our location privacy protection algorithm has better performance and is suitable for location privacy protection in mobile social networks.