1 / 31

A Simulation Study of P2P File Pollution Prevention Mechanisms

A Simulation Study of P2P File Pollution Prevention Mechanisms. Chia-Li Huang, Polly Huang Network & Systems Laboratory Department of Electrical Engineering National Taiwan University. Outline. Background Problem Methodology Simulation Environment & Results Conclusion.

chandler
Download Presentation

A Simulation Study of P2P File Pollution Prevention Mechanisms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. A Simulation Study of P2P File Pollution Prevention Mechanisms Chia-Li Huang, Polly Huang Network & Systems Laboratory Department of Electrical Engineering National Taiwan University

  2. Outline • Background • Problem • Methodology • Simulation Environment & Results • Conclusion

  3. Overview of P2P file sharing system • P2P file sharing system with search capability • Issue a query with keywords to search for a file A file in system Song title, length, encoding scheme of songA Different versions of songA Mp3, wma,… HashValue Hash function songA

  4. How a user searches for a file Responses for songA Peer1 P2P network Query for songA Randomly choose a source for download

  5. Pollution in file sharing system • Definition of a polluted file • Meta-data description doesn’t match its content! • Current P2P networks are full of polluted files [1] • Unintentional • Intentional • [1] J. Liang, Y. X. R. Kumar, and K. Ross, • “Pollution in p2p file sharing systems,” in Proceedings of IEEE Infocom, 2005

  6. Problem • Pollution in P2P system results in the following problems • Reduce content availability • Increase redundant traffic • There are different anti-pollution mechanisms existing • Which one is better?

  7. Methodology • Simulation study on anti-pollution mechanisms • Extending a P2P simulator [2] • Existing anti-pollution mechanisms • Peer reputation system • Choose a reputable peer to download file • EigenTrust [3] • Object reputation system • Choose a reputable version of a file to download • Credence [4] • Different pollution attacks • User behavior [2] M. Schlosser and S. Kamvar, “Simulating a file-sharing p2p network ,” In Proc.of SemPGRID 2003 [3] S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina, “The eigentrust algorithm for reputation management in p2p networks”, in Proceedings of the Twelfth International World Wide Web Conference, [4] K. Walsh and E. G. Sirer, “Experience with an object reputation system for peer-topeer filesharing”, in Proceedings of Networked System Design and Implementation (NSDI), May 2006.

  8. Peer Reputation System : EigenTrust • Rate a peer by it’s uploading history from the whole system Local reputation (Cij) • Global reputation(Ti) Peeri Peerj Good file Cij=

  9. Peer Reputation System : EigenTrust • Rate a peer by it’s uploading history from the whole system • Choose a reputable peer to download Local reputation (Cij) • Global reputation(Ti) Peeri Peerj T2 T3 Bad file Peer3 Peer2 Cij= C31 C21 A peer will store a list of local reputations C 12 Peer1 Peer 2 T1 =? T1 = C21* T2 + C31*T3 Peer 1 C 14 Peer 4

  10. Object Reputation System : Credence • Calculate an object (file) reputation by weighted votes • After download  vote it as clean or polluted Query of song A Vote-gather Query of song A P2 P3 P1 P4 P5

  11. Object Reputation System : Credence • Calculate an object (file) reputation by weighted votes • After download  vote it as clean or polluted • Choose a reputable version for download Received Responses of P1 Responses of song A Vote-responses of song A P2 Version1 Votep2 Version1 P3 Votep3 Version2 P1 Votep4 P4 Positive correlation Negative correlation Votep5 P5  random choose a source

  12. Pollution Attacks • Prevalent pollution attacks [5] • Decoy Insertion • Hash Corruption Decoy Insertion Hash Corruption A clean file of SongA • [5] F. Benevenuto, C. Costa, M. Vasconcelos, V. Almeida, J. Almeida, and M. Mowbray, • “Impact of peer incentives on the dissemination of polluted content”, in SAC ’06

  13. User Behavior • Slackness [6] • A period of time between download completion and quality check • Bimodal distribution • Awareness [6] • The probability that a user can correctly recognize a file being polluted • No clear characteristic is observed • high-awareness prob. = 0.8 • low-awareness prob. = 0.2 • [6] U. Lee, M. Choi, J. Cho, M. Y. Sanadidi, and M. Gerla, “Understanding pollution dynamics in p2p file sharing”, • in Proceedings of the 5th International Workshop on Peer-to-Peer Systems (IPTPS’06), 2006

  14. Outline • Background • Problem • Methodology • Simulation Environment & Results • Conclusion

  15. Simulator Description • P2P Query Cycle based simulator • In a cycle, each peer issues one query and repeats downloading until satisfied • Extension • Types of attacks • Decoy Insertion, Hash Corruption • Anti-Pollution mechanisms • EigenTrust, Credence • User behavior • Slackness, awareness

  16. Simulation Scenario

  17. Simulation Setup Table 1. File size distribution of P2P traffic [10] [8] S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina, “The eigentrust algorithm for reputation management in p2p networks”, in Proceedings of the Twelfth International World Wide Web Conference, [9] K. Walsh and E. G. Sirer, “Experience with an object reputation system for peer-topeer filesharing”, in Proceedings of Networked System Design and Implementation (NSDI), May 2006. [10] N. Leibowitz, M. Ripeanu, and A. Wierzbicki, “Deconstructing the Kazaa network”, Internet Applications. WIAPP 2003. Proceedings. The Third IEEE Workshop

  18. Critical Evaluation Parameters Evaluate different anti-pollution mechanisms under the following scenarios

  19. Evaluation metrics • Successful Downloading Rate (per cycle) • Redundant Traffic (per cycle) • Reduced traffic Ratio(compared to randomly selection ) Total successful downloads Reduced redundant traffic by using Mj Total trials of downloads Redundant traffic generated by random selection

  20. Simulation Result • Compare the performance of different anti-pollution mechanisms under different scenarios • EigenTrust • Credence • Random

  21. Successful Downloading Rate Credence is more sensitive to the type of attacks Credence identifies a clean version before download EigenTrsut rates on peers, not the hashvalue Converge after 100 cycles EigenTrust > Credence Credence > EigenTrust Under Decoy-Insertion attack Under Hash-Corruption attack

  22. Observation 1 : User awareness Reasons: 1. Fewer peers share clean files 2. Less peers correctly operate the reputation system Credence EigenTrust

  23. Observation 1 : User awareness Reasons: 1. Fewer peers share clean files 2. Less peers correctly operate the reputation system Credence EigenTrust User awareness is critical on anti-pollution mechanisms

  24. Observation 2 : User slackness Pollution held by a user longer has more chances to be download User slackness has negative effect on Anti-pollution mechanisms

  25. Discussion • User behavior has significant effect on anti-pollution mechanisms • Credence performs better under Decoy Insertion, while Eigentrust performs better under Hash Corruption • Type of attacks can’t be predicted • Suggest a hybrid anti-pollution mechanism

  26. Hybrid Anti-pollution Mechanism Response -list Query for songA P2P network Step2: Select a reputable peer by peer reputation mechanism Step1: Select a reputable version by object reputation mechanism

  27. Successful Downloading Rate Ensure both a reputable version and a source  confront different types of attacks Decoy Insertion Hash Corruption

  28. Successful Downloading Rate Ensure both a reputable version and a source  confront different types of attacks Hybrid mechanism performs the best under both attacks Decoy Insertion Hash Corruption

  29. Reduced-Traffic Ratio • Hybrid mechanism generate more control traffic • Trade-off between pollution traffic & control traffic The trade-off is worthwhile Decoy Insertion Hash Corruption

  30. Conclusion • Both peer reputation and object reputation system are necessary • User behavior has significant influence on anti-pollution mechanisms

  31. Thank you!

More Related