Toward Secured Social Tagging Systems

Social tagging systems provide an open platform for users to share and annotate their resources such as photos and URLs. Due to their open nature, however, these systems present a security problem. Malicious users may try to distort the systems behavior by inserting false user profiles. This paper addresses the problem of security and robustness of social tagging systems. We outline a framework to model the navigation of a tagging system. Using our framework we classify attack models and identify different kinds of potential attacks to a social tagging system. We implement two of our attack models and study the impact of attacks on existing algorithms used by tagging systems.