Trust management and data protection for online social networks

Online social networks (OSNs) have been an integral aspect of daily life. Most people use OSNs such as Facebook and Twitter to communicate and exchange information. Numerous analytical methods have been applied to OSNs by active exploration of their graph structure, like clustering analyses for automatic online group detection and node impact analyses of awareness of influential nodes within social networks. On OSN, a large-scale attack was launched by creating a bogus profile and using it to propagate spam, malware, and phishing attacks. To overcome the above issues, we designed a trust management and data protection model (TM-A-DPM), in which the trust management identifies the trust factor accuracy to achieve confidentiality, integrity, and privacy. Experimental results determine the performance and computational efficiency as well as resolve the privacy threats such as data leakage and data forgery.