Colluder Detection in Commercial P2P CDNs Using Reputation Information

Peer-to-Peer Content Delivery Networks (P2P CDNs) have attracted considerable attention as a way to distribute digital contents to paid users in a scalable and dependable manner. However, due to its peer-to-peer nature, it faces threat from colluders who paid for the contents but illegally share the contents to unauthorized peers. the detection of colluders is an important task for commercial P2P CDNs to prevent contents leaking which is necessary to preserve the right of contents holders. in this paper, we propose two schemes to detect colluders in P2P CDNs. the objective of the schemes is to detect as many colluders as possible within a short time while avoiding misidentifications. the first scheme manages the reputation of peers by collecting reports from the participants and the second scheme improves the quality of colluder identification by applying a technique used in the system level diagnosis of autonomous systems. the performance of the proposed schemes is evaluated by simulations. the simulation results show that when 10% of peers are colluders, our schemes detect all colluders accurately before forming an illegal cluster of colluders.