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

Niu, Wenjia, Tong, Endong, Li, Qian, Li, Gang, Wen, Xuemin, Tan, Jianlong and Guo, Li 2016, Exploring probabilistic follow relationship to prevent collusive peer-to-peer piracy, Knowledge and Information Systems, vol. 48, no. 1, pp. 111-141, doi: 10.1007/s10115-015-0864-1.

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Title Exploring probabilistic follow relationship to prevent collusive peer-to-peer piracy
Author(s) Niu, Wenjia
Tong, Endong
Li, Qian
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Wen, Xuemin
Tan, Jianlong
Guo, Li
Journal name Knowledge and Information Systems
Volume number 48
Issue number 1
Start page 111
End page 141
Total pages 31
Publisher Springer
Place of publication Berlin, Germany
Publication date 2016-07
ISSN 0219-1377
0219-3116
Keyword(s) P2P piracy
behavior
time constraint
content feedback
Bloom filter
Summary 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.
Language eng
DOI 10.1007/s10115-015-0864-1
Field of Research 080109 Pattern Recognition and Data Mining
0801 Artificial Intelligence And Image Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076103

Document type: Journal Article
Collection: School of Information Technology
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