A Secure Framework for Protection of Social Networks from Information Stealing Attacks

Social networks are online applications that allow their users to connect by means of various link types. Since these sites gather extensive personal information, there is a promising chance for leakage of personal information and inference attacks. To prevent the social networks from such attacks, a secure framework is proposed here. As first step, the social network is modeled as a connected graph where nodes and edges represent users of network and relationships among them respectively. Then three kinds of learning methods are applied for modeling the inference attacks.