Mobile Social Network Privacy Protection Method Oriented To Multi-level Structure

With the rapid development of mobile communication devices and the rapid rise of social networking platforms,more and more users like to share their daily lives through mobile social networks.These daily routines include geographic location and semantics as well as users’ social relationships.These three levels constitute A multi-layered mobile social network.The three levels are closely related.If joint mining is carried out,it is very easy to cause the privacy of users to be leaked.Therefore,secure mobile social network data publishing has become the focus of research in the field of data publishing.This thesis proposes three privacy protection methods for the three levels of mobile social networks.In the social network layer,this paper aims at the problem of low data availability caused by node weights that are not considered in the existing privacy protection methods for publishing uncertain graphs,and proposes a social network privacy protection method oriented to objective empowerment of nodes.This method uses the entropy method to objectively weight the nodes,and then uses the Laplacian mechanism to add noise to the edges and convert the noise to the probability of the edges,and finally generate and publish an uncertain graph.At the location social network layer,this paper proposes a location privacy protection method based on hypergraph clustering under mobile social networks.According to the user’s common location,the hypergraph clustering method is used to cluster users,and the users are divided into several communities.An indeterminate graph privacy protection algorithm is used to protect the privacy of the user’s location.In the semantic social network layer,this paper proposes a semantic privacy protection method based on hypergraph clustering in mobile social networks.This method uses the hypergraph clustering method to cluster users according to the common comment objects of users,and divides the users into several communities,Use the uncertainty graph privacy protection method to protect the semantic privacy in the community.The above three methods proposed in this paper transform the multi-level structure of mobile social networks into uncertain graphs for privacy protection.Through comparative experiments,the three methods all ensure data privacy and have high data availability in their respective levels,so that the final released data has a better privacy protection effect.