Yitzchak Rosenthal

P2P Mechanism Design: Incentives in Peer-to-Peer SystemsPaper By: Moshe Babaioff, John Chuang and Michal

Feldman

Types of P2P networks

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P2P network applicationsFile downloading (e.g. BitTorrent, Gnutella, etc.)Video streamingMANETs

P2P IssuesPrivate 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 RidingPeers try to get use OF network without providing services TO

network(e.g. downloading data from peers without uploading to peers)

WhitewashingIf 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 AttacksMultiple IDs by same user that collude with each other

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Addressing the problem through “Incentives”

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Provide “incentives” to peers to follow the rules

Types of incentivesCurrency (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) - KaZaAPeers get a better reputation when they provide TO the networkPeers with better reputation get better download speed

Barter (BAR) - BitTorrentScalable - 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

peersPeers who have a chunk exchange with peers who have other

chunks.

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Case study: File Sharing Networks

One shot game

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In a ONE SHOT GAME - free Riding is the dominant strategySimilar to one shot Prisoner’s Dilemma (PD) where

dominant strategy is to defect.No downsides for cheatingNo loss of reputationNo way to spend any “income”

Other approaches

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Direct reciprocity can be better, butIn 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.

Dilution of effects of direct reciprocity with large population.

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Reputation

Reputation

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Areas that reputation work:Evolutionary biologyOnline marketplaces (e.g. eBay)FileSharing - eg. KaAzA – files who upload have

better reputation scores and get higher priority when downloading

Eigentrust algorithmUses “transitive trust relationships” to aggregates

local “trust values” to form “global” trust valuesSimilar to “page rank” in Google

Credence algorithmExtends “trust” from peers to objects in p2p system

to defend against pollution and poisoning

Minimalist P2P model (no reputation)

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Each peer i has type θ = generosity = amount that peer will contribute to system

x = # of contributors to systemContribution cost per peer = 1/xDecision of rational peer:

See graph on next slide

Miminimalist P2P model - costs

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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 θm.

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.

Solve for x1 and x2

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Solve for

Benefits

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Benefit proportional to contribution level – αPerformance of System:

Ws = αx – (1/x)x = αx -1 (note 1/x is used instead of 1/ θ )

System will still collapse if maximal generosity is low

Reputation system

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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:

Analysis with reputation

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Q – individual benefitR – reduced contribution costT – threat

Contributor performance : Q – R =

Free Rider performance :

System Performance:

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Analysis of Barter Based System (BitTorrent)

Principal Agent Model

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N – set of agentsn : # of agentsAi = {0,1} : set of possible actions for each agent,

i ∈ N – a specific agentSet of n agents, N