Free-riding and incentivesin P2P systems

name: Michel Meulpolderdate: September 8, 2008event: Tutorial IEEE P2P 2008

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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.]

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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

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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

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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

+

-

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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

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Malicious behavior

• Improving one’s own situation:• Cheating• Whitewashing• Collusion• Hitchhiking• Sybil attack

• Disrupting the system:• Spam• Fake content• Low quality content

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Cheating

• Improving one’s own situation by:• Spreading false information• Using an alternative protocol / client

$$$

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Whitewashing

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

$$$

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Collusion

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

$$$

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Hitchhiking

• A peer making use of another peers high reputation with or without this peers knowledge

$$$

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Sybil attack

• Using multiple (cheap) identities to:• Perform `single user collusion’• Make use of aggregate free credits / points / free data

$$$$$$

$$$

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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

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Distributed examples

History Consistency Memory

Tit-for-tat Private Local Short

EigenTrust Shared Global Long

Karma Shared Global Long

Maxflow / BarterCast Shared Local (!) Long

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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

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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 Cn...

...

...

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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

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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’

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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

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BarterCast message protocol

• A peer spreads information to others about how much it uploaded/downloaded to/from whom

5 -> 9

1: 100 up 80 down2: 600 down6: 50 up

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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)

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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...

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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

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Questions?