Robust content-driven reputation

In content-driven reputation systems for collaborative content, users gain or lose reputation according to how their contributions fare: authors of long-lived contributions gain reputation, while authors of reverted contributions lose reputation. Existing content-driven systems are prone to Sybil attacks, in which multiple identities, controlled by the same person, perform coordinated actions to increase their reputation. We show that content-driven reputation systems can be made resistent to such attacks by taking advantage of the fact that the reputation increments and decrements depend on content modifications, which are visible to all. We present an algorithm for content-driven reputation that prevents a set of identities from increasing their maximum reputation without doing any useful work. A variation of the algorithm ensures that the reputation of each identity which performs only non-useful work decreases. Here, work is considered useful if it causes content to evolve in a di...

• Type: Conference
• Year: 2008
• Where: CCS
• Authors: Krishnendu Chatterjee, Luca de Alfaro, Ian Pye
• Keywords: CCS 2008, Content-driven Reputation, Content-driven Reputation Systems, Contributions Gain Reputation, Security Privacy