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

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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.

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Notes

  1. http://en.wikipedia.org/wiki/Bloom_filter.

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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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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Science, Beijing, 100093, People’s Republic of China

    Wenjia Niu, Qian Li, Xuemin Wen, Jianlong Tan & Li Guo

  2. Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People’s Republic of China

    Endong Tong

  3. School of Information Technology, Deakin University, 221 Burwood Highway, Geelong, VIC, 3125, Australia

    Gang Li

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  1. Wenjia Niu
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  2. Endong Tong
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  3. Qian Li
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  4. Gang Li
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  5. Xuemin Wen
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  6. Jianlong Tan
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  7. Li Guo
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Corresponding authors

Correspondence to Endong Tong or Gang Li.

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Niu, W., Tong, E., Li, Q. et al. Exploring probabilistic follow relationship to prevent collusive peer-to-peer piracy. Knowl Inf Syst 48, 111–141 (2016). https://doi.org/10.1007/s10115-015-0864-1

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  • DOI: https://doi.org/10.1007/s10115-015-0864-1

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