Personalized Privacy Protection with Spatio-Temporal Features in Social Networks

After users publish the social data, the privacy information was available to an attacker through node re-identification. In real life, users have different privacy protection requirements. Due to the personalization of users' privacy needs, the protection technology of attacks with the same background knowledge is insufficient to meets users' privacy protection requirements. To solve the problem, this paper proposes spatio-temporal classification levels of social network data background knowledge, and provides corresponding protection methods for different background knowledge intensification of each level. Specifically, three levels of protection requirements are defined on the basis of the gradually increasing background knowledge of the spatio-temporal features of the attacker. The effectiveness of the framework is verified by a large number of experiments.