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A Reputation Scheme Combining Trust and Incentive for P2P File Sharing

A Reputation Scheme Combining Trust and Incentive for P2P File Sharing. Mao Yang ， Qinyuan Feng ， Yafei Dai ， Zheng Zhang Peking University , Beijing, China Microsoft Research Asia, Beijing, China ICDCS2007 ， Toronto ， Canada. Li Xiaoming, Peking University. Outline. Motivations

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A Reputation Scheme Combining Trust and Incentive for P2P File Sharing

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  1. A Reputation Scheme Combining Trust and Incentive for P2P File Sharing Mao Yang，Qinyuan Feng，Yafei Dai，Zheng Zhang Peking University, Beijing, China Microsoft Research Asia, Beijing, China ICDCS2007，Toronto，Canada Li Xiaoming, Peking University

  2. Outline • Motivations • The problem • Proposal for a solution • An experiment to understand some features of the solution • Conclusions

  3. Motivations from two ends Philosophical Making “to be trusted” be part of incentive for living in a p2p world Technical (practical) Enrich initial trust-matrix M with richer information (for computational accuracy as well as efficiency) Eigen-trust Muilt-trust

  4. The “basics” of P2P file sharing system • “Axiom 1”: a p2p file sharing system is in a healthy state if it has enough number of users and the number does not decrease. • in a macro view • “Axiom 2”: the only reason for an individual being a p2p user is to get what he wants easily. • People tend to be free riders for public goods. • After all, “free riding” is a fundamental phenomena in human society (Olson, Mancur 1966, The Logic of Collective Action. Cambridge University Press )

  5. The “basics” of P2P file sharing system • “Law 1”: a volunteer-based p2p system is in a healthy state if it has enough good resources that can be easily accessed • A: availability (quantity of quality resources) • B: accessibility (high bandwidth, easy to find, not confusing, not frustrating) • “Law 2”: a volunteer-based p2p system being in a healthy state is not because we have eliminated free-riders and colluders, etc.

  6. How to keep the users: the core concern of a p2p system owner But，… • Selfish users • free-riders: download a lot, upload a little (purposely or not), negative to A • Whitewashers, colluders: try to take advantage of incentive policy in a p2p system without actual contribution, negative to both A and B. • “Malicious” users • Intentionally upload fake files, negative to B • To take advantage of traffic-based incentive policy • To pollute the resource environment, so that innocent users get frustrated.

  7. Two general measures to keep users • Incentives (awards)  improve A • provide some kind of reward for contributions • such as points in Maze, TFT in BT and eMule • Trust  improve B • Knowing the trustworthy of users or files, user has more confidence about what he is getting • such as EigenTrust

  8. A duality formulation of award and trust • Award：attribute of a downloader so that a uploader can use it to differentiate service to multiple downloaders • Trust：attribute of a uploader so that a downloader can use it to differentiate service from multiple uploaders

  9. Why one needs a high trust ? • Award is useful – get better service, so one benefit from it directly • Trust is also useful – when combined with award – “higher trust  higher award”, so one benefit from it indirectly • In Chinese old saying, “good return for good behavior, and bad return for bad behavior”（善有善报，恶有恶报）

  10. Reputation = f (award, trust) • Reputation = f (traffic–based award, trust) • In Chinese, respect = f (hats, words) • 一个人得到的社会尊重 = f (光环，口碑) Globalor pair wise ?

  11. Reputation matrix, r(i,j) = fij(award, trust) • Award – traffic based：more upload, higher award – easy to measure • What about trust (what is good behavior) ? • Vote on files honestly • Retain good files for long time • Remove bad files quickly • Upload good files • Rank users honestly • Granularity: users vs. files; • Measurement: explicit vs. implicit File based Download based User based

  12. From behavior to trust value （1） • Vote on files honestly • EEik: an explicit value for file k assigned from user i • Retain good files for long time, and remove bad files quickly • IEik: an implicit value for file k due to action of user i • Combing EEik and IEik to form Eik, the opinion of user i to the file k.

  13. File based trust relationship FTij: file based trust relationship between users Ui and Uj The more they agree on files, the stronger trust they have (symmetric). Fij: the intersection of files evaluated by Ui and Uj If he is a good guy, he probably will behave well. Thus if I want him thinks me good (so I can get things from him easily), I should also behave well.

  14. From behavior to trust（2） • Upload good files • Eik*Sk, the trustworthy of file k considered by user i. Summing over k from one user j, we get the opinion of user i about user j based on downloads • Thus, we will not have simplistic trafic-based award for upload. Rather, the contribution in terms of volume is attributed with trust.

  15. Download volume combined with trust For all files downloaded from j Normalized with all users

  16. From behavior to trust (3) • Rank users honestly UTij: Ui’s rank of Uj Uall: all the users An integrated one-step trust matrix (TM) : TM(i,j): how much i trusts j based on the three factors

  17. From trust to reputation (multi trust) We’d like to know the (non zero) density of TM with respect to the probability of file evaluations Higher the density, lower the n Also note that density of TM is higher than that of FM

  18. The emulation based on Maze logs • Logs of one month: 103K users, 24M download/upload actions, 395K files • <t, i, j, f>: at time t, i downloaded file f from j • Following the time sequence of the logs • Set a=0, b=0 /** we watch for a/b • For each observed log <t, i, j, f> • b++ • if there exists file that has been evaluated by both i and j before t, then a++, else evaluate (vote) file f with probability k% on behave of i. • For each end of day, record a/b

  19. Note, this is only an approximation of FM density Experiments for different k’s

  20. The use of reputation values Gik: goodness of file k reported to user i, based on collective evaluations and reputations Ku: the set of users that have evaluated k Fake files identification. If i wants to download file k. To get “true” value of Gik，i should try to make RMij as true as possible Naturally, it can also be used as priority for downloading

  21. Conclusions • We proposed a pair wise reputation scheme combining trust and incentive • philosophically, encouraging user to behave well in a file sharing system for his own good • technically, richer direct trust relation for accuracy and ease of computation. • Some of the possible attack and implementation issues are also discussed in the paper.

  22. Future works Fine tune of the parameters with more emulations More direct relation between “good behavior” (especially those subjective behavoir) and reputation values How to let the “quality of ith row” be reflected on the ith colunm ? Deployment on real systems

  23. Q & A • lxm@pku.edu.cn

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