SNS Privacy Protection based on the ELM Integration and Semi-supervised Clustering

Social network service (SNS) is a new emerging Web application. With the growth of SNS in application, the web security is facing more serious threats and the leak of individual privacy is a major one. Because of vast number of unlabeled data and small amount of labeled data in SNS web, the availability of data is relatively poor for the study of SNS privacy preservation. In order to solve the above problem, this paper proposed an ELM ensemble algorithm based on Bagging combined with semi-supervised Seeds set clustering for privacy preserving. The main process is as follows: first, the ensemble ELM is used to label the unlabeled data to enlarge the scale of Seeds set; second, the Seeds set is used to initialize the center of clustering; and finally, the algorithm adopts semi-supervised clustering to achieve K-anonymity. Experimental results show that the method can improve the usability of the released data while preserving privacy.