Influential Incremental Learning-Based Privacy Preservation for Social Network Information

Abstract

Social network influence dissemination focuses on employing a small number of seed sets to generate the most significant possible influence in social networks and considers forwarding to be the only technique of information transmission, ignoring all other ways. Users, for example, can post a message via this mode of distribution (called para), which is difficult to trace, posing a danger of privacy leakage. This research tries to address the aforementioned issues by developing a social network information transmission model that supports the paranormal relationship. It suggests a way of disseminating information called Local Greedy, which aids in the protection of user privacy. Its effect helps to reconcile the conflict between privacy protection and information distribution. Aiming at the enumeration problem of seed set selection, an incremental strategy that supports privacy protection is proposed to construct seed sets to reduce time overhead; a local influence subgraph method of computing nodes is given to estimate the influence of seed set propagation quickly; the group satisfies the constraints of privacy protection, and a plan is proposed to deduce the upper limit of the probability of node leakage state, avoiding the time cost of using the Monte Carlo method using the crawled Sina Weibo dataset. Experimental verification and example analysis are carried out, and the results show the effectiveness of the proposed method.