Evaluating the Impact of Attacks in Collaborative Tagging Environments

The proliferation of social Web technologies such as collaborative tagging has led to an increasing awareness of their vulnerability to misuse. Attackers may attempt to distort the system's adaptive behavior by inserting erroneous or misleading annotations, thus altering the way in which information is presented to legitimate users. Prior work on recommender systems has shown that studying different attack types, their properties and their impact, can help identify robust algorithms that make these systems more secure and less vulnerable to manipulation.Unlike traditional recommender systems, a tagging system includes multiple retrieval algorithms to facilitate browsing of resources, users and tags. The challenge is, therefore, evaluating the impact of various types of attacks across different navigation options. In this paper we develop a framework for characterizing attacks against tagging systems. We then propose a methodology for evaluating their global impact based on PageRank. Using real data from a popular tagging systems, we empirically evaluate the effectiveness of several attack types. Our results help us understand how much effort is needed from an attacker to change the behavior of a tagging system and which attack types are more successful against such systems.