Data Mining Application: Attack Resistant Trust Metrics |
This Dissertation characterizes the space of trust metrics, under both the scalar assumption where each assertion is evaluated independently, and the group assumption where a group of assertions are evaluated in tandem. We present a quantitative framework for evaluating the attack resistance of trust metrics,and give examples of trust metrics that are within a small factor of optimum compared to theoretical upper bounds. We discuss experiences with a real world deployment of a group trust metric, the Advogato website. Finally, we explore possible applications of attack resistant trust metrics, including using it as to build a distributed name server, verifying metadata in peer-to-peer networks such as music sharing systems, and a proposal for highly spam resistant e-mail delivery.