1. Yitzchak Rosenthal P2P Mechanism Design: Incentives in Peer-to-Peer Systems Paper By: Moshe Babaioff, John Chuang and Michal Feldman

2. Types of P2P networks 2 P2P network applications File downloading (e.g. BitTorrent, Gnutella, etc.) Video streaming MANETs

3. P2P Issues Private Information Many P2P protocols require clients to divulge “private information”. Examples: Amount of bandwidth a client has for uploading files. List of files/data client has for uploading Clients may choose NOT to divulge private information in order to exploit the network for its own gain. Free Riding Peers try to get use OF network without providing services TO network (e.g. downloading data from peers without uploading to peers) Whitewashing If multiple identities can be created for free then an “evil” user can destroy an identity once it has been recognized as not following the rules and exploiting the network Sybil Attacks Multiple IDs by same user that collude with each other 3

4. Addressing the problem through “Incentives” 4 Provide “incentives” to peers to follow the rules Types of incentives Currency (CUR) - Mojonation Peers earn “currency” when providing TO the network. The “currency” can be “spent” in order to get services/data FROM the network Reputation (REP) - KaZaA Peers get a better reputation when they provide TO the network Peers with better reputation get better download speed Barter (BAR) - BitTorrent Scalable - doesn’t keep state information (CUR and REP do) Files are broken into many equal size chunks “seeder” peer distributes DIFFERENT chunks to many different peers Peers who have a chunk exchange with peers who have other chunks.

5. 5 Case study: File Sharing Networks

6. One shot game 6 In a ONE SHOT GAME - free Riding is the dominant strategy Similar to one shot Prisoner’s Dilemma (PD) where dominant strategy is to defect. No downsides for cheating No loss of reputation No way to spend any “income”

7. Other approaches 7 Direct reciprocity can be better, but In large population, effect of direct reciprocity is diluted since the odds of interacting again with same peer is low (Friedman, et al) (See next slide) Enforce direct interaction with limited number of peers (BitTorrent) Reputation systems – introduces state – may not scale as well How to deal with newcomers: Dissuade whitewashing by Cooperate with strangers with a fixed probability, p, is not robust against white washers Better approach is adjust p based on frequency of past cooperation with strangers. This works better for a small turnover rate.

8. Dilution of effects of direct reciprocity with large population. 8

9. 9 Reputation

10. 10 Areas that reputation work: Evolutionary biology Online marketplaces (e.g. eBay) FileSharing - eg. KaAzA – files who upload have better reputation scores and get higher priority when downloading Eigentrust algorithm Uses “transitive trust relationships” to aggregates local “trust values” to form “global” trust values Similar to “page rank” in Google Credence algorithm Extends “trust” from peers to objects in p2p system to defend against pollution and poisoning

11. Minimalist P2P model (no reputation) 11 Each peer i has type θ = generosity = amount that peer will contribute to system x = # of contributors to system Contribution cost per peer = 1/x Decision of rational peer: See graph on next slide

12. Miminimalist P2P model - costs 12 Y axis is # of contributors to system X axis is generosity level x1, x2 (on Y axis) and zero are equilibria of system X2 is NOT a stable equilibrium Generosity θ is uniformly distributed beween 0 and θ m. Straight line is CDF of percent of peers who will contribute at a certain price level. Curved line is the model of the cost per contributor.

13. Solve for x1 and x2 13 Solve for

14. Benefits 14 Benefit proportional to contribution level – α Performance of System: Ws = α x – (1/x)x = α x -1 (note 1/x is used instead of 1/ θ ) System will still collapse if maximal generosity is low

15. Reputation system 15 Catch free riders with probabilty, p, an eliminate free riders from system OR catch free riders with probability 1 and peanalize free riders with (1-p) times reduced service of contributor Load placed on system decreases to : So contribution cost becomes:

16. Analysis with reputation 16 Q – individual benefit R – reduced contribution cost T – threat Contributor performance : Q – R = Free Rider performance : System Performance:

17. 17 Analysis of Barter Based System (BitTorrent)

18. Principal Agent Model 18 N – set of agents n : # of agents Ai = {0,1} : set of possible actions for each agent, i ∈ N – a specific agent Set of n agents, N