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Robust Incentives via Multi-level Tit-for-tat

In the realm of P2P file sharing, effective incentive mechanisms are crucial for fostering collaboration among users. This paper discusses the challenges of genuine and artificial incentives, placement of collaborative indicators, and highlights the flaws in traditional algorithms. We present the Maze P2P system, which integrates a multi-trust algorithm inspired by Tit-for-Tat and EigenTrust to improve user contributions and address collusion issues. The evaluation reveals the system's potential to enhance sharing dynamics while combating malicious behaviors, emphasizing the importance of user reputation and behavior analysis in incentive distribution.

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Robust Incentives via Multi-level Tit-for-tat

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  1. IPTPS, Feb. 2006 Robust Incentives via Multi-level Tit-for-tat Qiao Lian, Zheng Zhang (MSRA) Yu Peng, Mao Yang, Yafei Dai, Xiaoming Li (PKU)

  2. P2P file-sharing needs incentives to work • genuine incentives: must collaborate/share to benefit • E.g. block exchange in BT • Problems: only works within a large session • Nearly 80% sessions contain 2 peers only, i.e. there is only one downloader • No one else to collaborate with!

  3. A simple breakdown of the spectrum • Artificial incentive: • Produce/record evidence of collaboration for future reference Brittle to collusion and other problems Incentives Shared history Private history Artificial incentives genuine incentives subjective Non-subjective Absolute contribution (e.g. Maze) The sum of contribution from the perspective of other peers, weighted by their reputation (e.g. EigenTrust)

  4. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  5. Maze:architecture • A sends query • server responses with file / replica info • A sends download requests • B and C response with file data • B and C upload traffic log centralize maintained index / membership user cloud Our work starts from these logs C A B

  6. Vital statistics • Popular • Population: 1.4 million registered accounts; 30,000+ online users • More than 200 million files • More than 13TB (!) transfer everyday • Completely developed, operated and deployed by an academic team • Logs added since the collaboration w/ MSRA in 2004 • Enable detailed study at all angles

  7. Maze：Incentive Policies • New users: points == 4096 • Point change: • Uploads: +1.5 points per/MB • Downloads: at most -1.0 point/MB • Gives user more motivation to contribute • Benefit of high point • Climbing ladder  social status • Service differentiation: • Order download requests by T = Now – 3log(Point) • Users with P < 512 have a download bandwidth of 200Kb/s • Available in Maze5.0.3; extensively discussed in Maze forum before implemented

  8. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  9. What is collusion • Definition (Webster dictionary): • secret agreement or cooperation especially for an illegal or deceitful purpose • And in the Maze context: • Multiple peers collude to defeat the incentive system • What makes the study hard: • Even with all the traffic logs, we will never know for sure • But we can identify suspicious colluding patterns • See our technical report for more details

  10. the collusion workingset 221,000 pairs whose duplication degree > 1 the top 100 pairs with most redundant traffic • Repeat traffic detector • Hint: colluders are lazy • for peer pair link: duplication degree = total traffic / unique data

  11. A closer look… Ted: 3.8TB Sam: 47GB Ingrid: 78GB Mary: 73GB Star-shape collusion(spam account): colluding + whitewashing account (Fred, Gary) (Olga, Pam) Pair-wise collusion (David, Alice, Quincy) e.g. Alice uploads MSDN DVD image (~3GB) for 29 times (Harry, Cindy)

  12. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  13. What about EigenTrust? • EigenTrust: clone of PageRank • Basic idea: • Consider recommender’s reputation • Trust matrix M: • mi,j: trust of peer i to peer j (e.g download quantity) • normalize each row of M: • EigenTrust vector: • The left principal eigenvector T • The rank of peer i is Ti

  14. What about EigenTrust? A 9GB 9GB 1GB 10GB B • EigenTrust: clone of PageRank • Basic idea: • Consider recommender’s reputation • Trust matrix M: • mi,j: trust of peer i to peer j (e.g download quantity) • normalize each row of M: • EigenTrust vector: • The left principal eigenvector T • The rank of peer i is Ti 1GB C 10GB 30GB

  15. False negative of EigenTrust How the leg-hugger has high score: leg-hugger Larry • Does the 734KB upload to Ted really matter? • No, Ted is an irrational user • It downloads only 124MB, but uploads 3.8TB.

  16. False positive of EigenTrust(local distributor Wayne) 5600GB Local distributor Wayne • Wayne is in a satellite cluster • Wayne uploads 290GB. • Its EigenRank equals to a peer in majority community with 10GB upload • Is it fair? • At least, Wayne should have high rank inside the satellite cluster. • We need personalized rank for each peer, e.g. Tit-for-Tat

  17. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  18. Private history: Tit-for-Tat • Idea: trust peers (friends) who has helped me before • Used in eMule and BitTorrent (the 2 popular P2P filesharing system)

  19. Private history: Tit-for-Tat • Idea: trust peers (friends) who has helped me before • Used in eMule and BitTorrent (the 2 popular P2P filesharing system) • Problem: extremely small coverage ???? Limited coverage even with longer history

  20. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  21. Multi-trust incentive algorithm • Idea: we need more than one tier of trust! • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list

  22. Multi-trust incentive algorithm • Idea: we need more than one tier of trust • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list

  23. Multi-trust incentive algorithm • Idea: we need more than one tier of trust • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list

  24. Multi-trust incentive algorithm • Idea: we need more than one tier of trust • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list

  25. Multi-trust incentive algorithm • Idea: needs more than one tier of trust • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list A B C D 1-hop friends 2-hop friends E 3-hop friends F other peers …… F A B E C D

  26. Multi-trust incentive algorithm • Idea: needs more than one tier of trust Mathematically answer: use full spectrum { M, M2, … M∞ } • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list M M2 M3

  27. Multi-trust incentive algorithm multi-trust: the full spectrum incentive algorithm • Evaluation: • Coverage: real trace driven simulation of one month • Effectiveness: statically evaluate the next 2 weeks traffic • Metric: colluder’s queue position at the data source peer Tit-for-Tat M∞T: EigenTrust { M, M2, … M∞ } Coverage Personalization

  28. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  29. Multi-trust incentive algorithmCoverage experiment • The coverage of {M, M2} is already good enough • We can choose using {M, M2, M∞}

  30. Multi-trust incentive algorithm: Effectiveness expr. methodology • Setup • Generating rank based on one months history • Evaluate the next two weeks • Metric: • We don’t have a global rank … • Queue Position at each source peer: • Source peers: who holds interested resource to me

  31. Multi-trust incentive algorithm: dealing with colluders Spam account colluder • 5/7 punish Ingrid equally • Peer 7 punishes more in multi-trust • Peer 4 punishes less in multi-trust since it downs from Ingrid Desirable: as good as EigenTrust Pair-wise colluder • 7/9 punish Cindy equally • 2/9 punish more in multi-trust • Friends get ahead!

  32. Multi-trust incentive algorithm:solve problems in EigenTrust False-negative False-positive Leg-hugger • 78% peers rank Larry lower • 22% are still affect by super peers Ted. Local distributor • Inside: • 2/3 peers promote Wayne’s rank • 1/3 is too young to know Wayne’s good • Outside: another friend

  33. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  34. Summary and Related Work

  35. Conclusion • EigenTrust and Tit-for-tat each have their own pitfall • Multi-trust as a hybrid achieves betterbalance

  36. Thank you Q&A

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