1. Free-riding and incentives in P2P systems name:Michel Meulpolder date:September 8, 2008 event:Tutorial IEEE P2P 2008

2. 2 Today’s P2P world Large populations High churn Flashcrowds Low rendez-vous probability (Less than 10% of peer pairs exchange data more than once) Small-world effect (Most peers interact with only a few others, while a few peers interact with a lot) Long tailed demand (Top 25% of peers account for more than 75% of demand) Long tailed file popularity [Feldman 2004, Pouwelse 2005, Piatek 2008, et.al.]

3. 3 Free-riding vs. cooperation Quality of P2P depends on cooperation Free-riding: obtaining resources from the system without contributing to it can be ‘strong’ or ‘weak’ Gnutella: 70% free-riders Free-riding leads to: Overall lower download speeds Unavailability of (rare / unique) files Trivial solution: central authority

4. 4 Central authority Kazaa, DC++, eMule, Maze,... Keep track of your behavior / what you share Punish / reward according to policy Sometimes leave users to decide Private BitTorrent trackers E.g., Oink, TVTorrents,... Based on user accounts, sometimes invites Keep track of upload, download, quality of content, etc. Offer rating, recommendation, metadata Ban users that do not meet the required standards

7. 7 Central authority pros/cons Good performance, many uploaders Quality can be enforced Balanced collections `Community feeling’ Central point of failure Administration overhead Trust in authority & privacy sensitive Limited scalability + -

8. 8 Research Challenge Creating a zero-server incentive mechanism Reward cooperation -> reduce free-riding Robust in today’s file-sharing world: Churn Long-tailed popularity Low rendez-vous Resistant against malicious behavior

9. 9 Malicious behavior Improving one’s own situation: Cheating Whitewashing Collusion Hitchhiking Sybil attack Disrupting the system: Spam Fake content Low quality content

10. 10 Cheating Improving one’s own situation by: Spreading false information Using an alternative protocol / client $$$

11. 11 Whitewashing Creating a fresh new identity in order to: Get rid of a negative reputation / account Profit of newcomers credit / points / free data $$$

12. 12 Collusion A group of peers collaborating to improve their situation by: Spreading false-positive information about each other Using an alternative protocol together $$$

13. 13 Hitchhiking A peer making use of another peers high reputation with or without this peers knowledge $$$

14. 14 Sybil attack Using multiple (cheap) identities to: Perform `single user collusion’ Make use of aggregate free credits / points / free data $$$

15. 15 Properties of incentive designs Architecture: central, hybrid, distributed History: private or shared Consistency: local or global Incentives validity (memory): long-term or short-term Usually independent: Reputation metric Incentive policy

16. 16 Distributed examples HistoryConsistencyMemory Tit-for-tatPrivateLocalShort EigenTrustSharedGlobalLong KarmaSharedGlobalLong Maxflow / BarterCastSharedLocal (!)Long

17. 17 Tit-for-tat Favor uploading to peers who provide the fastest rate in return during the same timeframe Valid now, between two peers, for the same file Can be `cheated’ -> BitTyrant, BitThief eMule: volume based tit-for-tat keep private history for future encounters with same peer favor peers with highest volume in the past suffers from low rendez-vous probability

18. 18 EigenTrust Provides a unique global trust value for every peer Based on distributed power iteration: Relies on pre-trusted peers to guarantee convergence and prevent collusion To prevent cheating, score managers compute the trust value of a peer instead of the peer itself Not feasible with high churn Vulnerable to hitchhiking C = ( ) 0.3 0.1 1.0 0.5 0.2 C n...

19. 19 Karma Every peer has a globally consistent karma value For each peer a set of bank nodes keeps track of its karma Objects are ‘bought’ from the lowest seller New peers are awarded a seed amount of karma Every peer is automatically a bank peer for others Periodically, inflation/deflation is corrected Can be vulnerable to churn and whitewashing No robust incentive to perform bank duties

20. 20 Maxflow based mechanisms A peer determines the contribution of another peer by computing the ‘flow’ from that peer to itself Maximal flow is limited by the `weakest links’

21. 21 BarterCast Message exchange protocol + maxflow reputation Exploits small-world effect Peers keep history of direct transactions + eye- witness accounts Provides local, subjective reputation similar to the social world

22. 22 BarterCast message protocol A peer spreads information to others about how much it uploaded/downloaded to/from whom 5 -> 9 1: 100 up 80 down 2: 600 down 6: 50 up

23. 23 Incentive/Reputation metrics Different ways to ‘measure’ a peers behavior: upload - download upload : download up_flow - down_flow normalized value scaled value (e.g., arctan)

24. 24 Incentive policies Measures taken to reward/punish peers according to their behavior, e.g.: Refuse uploading to peers with a ‘too low’ reputation Give higher speeds to ‘better’ peers Ban peers with low reputation from the system Give UI feedback as a stimulus to cooperate (top 10 list, ‘you are a good peer’, etc.) Etcetera...

25. 25 Economic challenges Using far more information than only uploading/downloading statistics: Quality of content Rarity / uniqueness Availability Speed P2P networking will become a market: People trade bandwidth, bits, and effort Possibilities for donation, altruism Many economic principles apply

26. 26 Questions?