Privacy Disclosure Identification in Social Networks

In online social network, users are much more likely to be influenced by their neighbor nodes and/or connected nodes which have comment interesting. Consequently, the nodes in social network are more easily to accepted the forwarded/recommended online applications from their familiar/trusted nodes. Although most online application have their own privacy policy and claim that they won´t disclose users´ privacy without users´ consent, also, the user can choose pushed information forbidden to the certain online application, user may receive some pushed information from the third party. In this scenario, it is clearly that the online application has disclosed users´ privacy information maybe with users´ preferences/behavior to the third party for their commercial interests. In this paper, we study a novel privacy disclosure phenomenon that privacy disclosure by users´ forwarded/recommended online application. We concrete with this privacy disclosure issue and propose a privacy identification method inspirited from Secure Multi-Party Computation (SMC) with both passive mode and active mode for this type of privacy disclosure identification. According to the analyze, our method can solve the privacy disclosure identification effectively.