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Knowledge and Information Systems

, Volume 48, Issue 1, pp 111–141 | Cite as

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

  • Wenjia Niu
  • Endong TongEmail author
  • Qian Li
  • Gang LiEmail author
  • Xuemin Wen
  • Jianlong Tan
  • Li Guo
Regular Paper

Abstract

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.

Keywords

P2P piracy Behavior Time constraint Content feedback Bloom filter 

Notes

Acknowledgments

This work was partially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences Grant (XDA06030200), the Securing CyberSpaces Research Cluster of Deakin University, Beijing Key Lab of Intelligent Telecommunication Software, Multimedia (No. ITSM201502), Guangxi Key Laboratory of Trusted Software (No. kx201418), and the Major Directionality Project of Chinese Academy of Sciences under Grant (KGZD-EW-102-1)

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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Institute of Information EngineeringChinese Academy of ScienceBeijingPeople’s Republic of China
  2. 2.Institute of MicroelectronicsChinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.School of Information TechnologyDeakin UniversityGeelongAustralia

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