Exploring probabilistic follow relationship to prevent collusive peer-to-peer piracy. Issue 1 (July 2016)

• Record Type: Journal Article
• Authors: Niu, Wenjia; Tong, Endong; Li, Qian; Li, Gang; Wen, Xuemin; Tan, Jianlong; Guo, Li
• Is Part Of: Knowledge and information systems. Volume 48:Issue 1(2016:Jul.)
• Page Start: 111
• Page End: 141
• Publication Date: 2016-07
• Subjects: P2P piracy -- Behavior -- Time constraint -- Content feedback -- Bloom filter; Expert systems (Computer science) -- Periodicals; Information storage and retrieval systems -- Periodicals; 006.33
• DOI: 10.1007/s10115-015-0864-1
• Languages: English
• ISSNs: 0219-1377

Abstract

Abstract P2Pcollusive piracy, where paid P2P clients share the content with unpaid clients, has drawn significant concerns in recent years. Study on thefollow relationship provides an emerging track of research in capturing thefollowee (e.g., paid client) for the blocking of piracy spread from all hisfollower s (e.g., unpaid clients). Unfortunately, existing research efforts on thefollow 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, aprobabilistic follow with content feedback model to fast discover and quantify the probabilisticfollow 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.