Exploring probabilistic follow relationship to prevent collusive peer-to-peer piracy

P2P collusive piracy, where paid P2P clients share the content with unpaid clients, has drawn significant concerns in recent years. Study on the follow relationship provides an emerging track of research in capturing the followee (e.g., paid client) for the blocking of piracy spread from all his followers (e.g., unpaid clients). Unfortunately, existing research efforts on the follow relationship in online social network have largely overlooked the time constraint and the content feedback in sequential behavior analysis. Hence, how to consider these two characteristics for effective P2P collusive piracy prevention remains an open problem. In this paper, we proposed a multi-bloom filter circle to facilitate the time-constraint storage and query of P2P sequential behaviors. Then, a probabilistic follow with content feedback model to fast discover and quantify the probabilistic follow relationship is further developed, and then, the corresponding approach to piracy prevention is designed. The extensive experimental analysis demonstrates the capability of the proposed approach.