--- _id: '3502' abstract: - lang: eng text: '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 resistant to such attacks by taking advantage of thefact 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. Here, work is considered useful if it causes content to evolve in a direction that is consistent with the actions of high-reputation users. We argue that the content modifications that require no effort, such as the insertion or deletion of arbitrary text, are invariably non-useful. We prove a truthfullness result for the resulting system, stating that users who wish to perform a contribution do not gain by employing complex contribution schemes, compared to simply performing the contribution at once. In particular, splitting the contribution in multiple portions, or employing the coordinated actions of multiple identities, do not yield additional reputation. Taken together, these results indicate that content-driven systems can be made robust with respect to Sybil attacks. Copyright 2008 ACM.' acknowledgement: 'This research has been partially supported by the CITRIS: Center for Information Technology Research in the Interest of Society.' author: - first_name: Krishnendu full_name: Krishnendu Chatterjee id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Luca full_name: de Alfaro, Luca last_name: De Alfaro - first_name: Ian full_name: Pye, Ian last_name: Pye citation: ama: 'Chatterjee K, De Alfaro L, Pye I. Robust content-driven reputation. In: ACM; 2008:33-42. doi:10.1145/1456377.1456387 ' apa: 'Chatterjee, K., De Alfaro, L., & Pye, I. (2008). Robust content-driven reputation (pp. 33–42). Presented at the AISec: Artificial Intelligence and Security, ACM. https://doi.org/10.1145/1456377.1456387 ' chicago: Chatterjee, Krishnendu, Luca De Alfaro, and Ian Pye. “Robust Content-Driven Reputation,” 33–42. ACM, 2008. https://doi.org/10.1145/1456377.1456387 . ieee: 'K. Chatterjee, L. De Alfaro, and I. Pye, “Robust content-driven reputation,” presented at the AISec: Artificial Intelligence and Security, 2008, pp. 33–42.' ista: 'Chatterjee K, De Alfaro L, Pye I. 2008. Robust content-driven reputation. AISec: Artificial Intelligence and Security, 33–42.' mla: Chatterjee, Krishnendu, et al. Robust Content-Driven Reputation. ACM, 2008, pp. 33–42, doi:10.1145/1456377.1456387 . short: K. Chatterjee, L. De Alfaro, I. Pye, in:, ACM, 2008, pp. 33–42. conference: name: 'AISec: Artificial Intelligence and Security' date_created: 2018-12-11T12:03:40Z date_published: 2008-10-31T00:00:00Z date_updated: 2021-01-12T07:43:54Z day: '31' doi: '10.1145/1456377.1456387 ' extern: 1 month: '10' page: 33 - 42 publication_status: published publisher: ACM publist_id: '2885' quality_controlled: 0 status: public title: Robust content-driven reputation type: conference year: '2008' ...