Credo : Thwarting collusion attacks in peer-to-peer content distribution systems

Current peer-to-peer content distribution networks do not effectively encourage users to act as seeders; this re sults in suboptimal behavior as many users disconnect immediatel y upon retrieving files. Reputation systems can incentivize u sers to remain active and seed files. Unfortunately, existing reput ation systems are unsatisfactory – they do not accurately capturea node’s net contribution in the face of Sybil and collusion at tacks. In this paper, we present Credo, a robust credit-based reput ation system with a novel reputation algorithm to compute node reputation based on the set of uploads reported by peer nodes . Credo’s algorithm employs two novel techniques:credit diversity and modeling good behavior, which can limit the reputation gains of colluding adversaries effectively. Using simulations,we show that Credo is resilient against attack and provides an effec tive incentive, allowing nodes with more upload contributions t o experience faster downloads. We have incorporated Credo in the Azureus BitTorrent client and show that overall system performance is significantly improved in the PlanetLab deployment. Additionally we show that our implementation scales well and is capable of handling systems with large numbers of nodes.