The eigentrust algorithm for reputation management in p2p networks
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The eigentrust algorithm for reputation management in p2p networks l.jpg

The EigenTrust Algorithm for Reputation Management in P2P Networks

Sepandar D. Kamvar

Mario T. Schlosser

Hector Garcia-Molina

Stanford University


Problem l.jpg
Problem Networks

  • Problem:

    • Reduce inauthentic files distributed by malicious peers on a P2P network.

  • Motivation:

“Major record labels have launched an aggressive new guerrilla assault on the underground music networks, flooding online swapping services with bogus copies of popular songs.”

-Silicon Valley Weekly


Problem3 l.jpg

0.9 Networks

0.1

Problem

  • Goal: To identify sources of inauthentic files and bias peers against downloading from them.

  • Method: Give each peer a trust value based on its previous behavior.


Some approaches l.jpg
Some approaches Networks

  • Past History

  • Friends of Friends

  • EigenTrust


Terminology l.jpg

t Networks3=.5

C12=0.3

C23=0.7

t1=.3

C21=0.6

t2=.2

C14=0.01

t4=0

Terminology

Peer 3

  • Local trust value:cij.The opinion that peer i has of peer j, based on past experience.

  • Global trust value: ti.The trust that the entire system places in peer i.

Peer 1

Peer 2

Peer 4


Local trust values l.jpg
Local Trust Values Networks

Cij=

  • Each time peer i downloads an authentic file from peer j, cij increases.

  • Each time peer i downloads an inauthentic file from peer j, cij decreases.

Peer i

Peer j


Normalizing local trust values l.jpg

Peer 1 Networks

C12=0.9

Peer 2

C14=0.1

Peer 4

Peer 4

Peer 2

Peer 1

Normalizing Local Trust Values

  • All cij non-negative

  • ci1 + ci2 + . . . + cin = 1


Local trust vector l.jpg

Peer 1 Networks

C12=0.9

C14=0.1

Peer 2

Peer 4

Peer 4

c1

Peer 2

Peer 1

Local Trust Vector

  • Local trust vector ci:contains all local trust values cij that peer i has of other peers j.


Some approaches9 l.jpg
Some approaches Networks

  • Past History

  • Friends of Friends

  • EigenTrust


Past history l.jpg

? Networks

?

?

?

?

Peer 6

?

Peer 4

Peer 1

Past history

  • Each peer biases its choice of downloads using its own opinion vector ci.

  • If it has had good past experience with peer j, it will be more likely to download from that peer.

  • Problem: Each peer has limited past experience. Knows few other peers.


Friends of friends l.jpg

Peer 6 Networks

Peer 4

Peer 1

Peer 2

Peer 8

Friends of Friends

  • Ask for the opinions of the people who you trust.


Friends of friends12 l.jpg

Peer 4 Networks

Peer 1

Peer 2

Peer 8

Peer 4

Friends of Friends

  • Weight their opinions by your trust in them.


The math l.jpg

What they think of peer k. Networks

And weight each friend’s opinion by how much you trust him.

Ask your friends j

.1

.5

0

0

0

.2

.1

.3

.2

.3

.1

.1

0 .2 0 .3 0 .5 .1 0 0 0

.2

The Math


Problem with friends l.jpg
Problem with Friends Networks

  • Either you know a lot of friends, in which case, you have to compute and store many values.

  • Or, you have few friends, in which case you won’t know many peers, even after asking your friends.


Dual goal l.jpg
Dual Goal Networks

  • We want each peer to:

    • Know all peers.

    • Perform minimal computation (and storage).


Knowing all peers l.jpg
Knowing All Peers Networks

  • Ask your friends: t=CTci.

  • Ask their friends: t=(CT)2ci.

  • Keep asking until the cows come home: t=(CT)nci.


Minimal computation l.jpg
Minimal Computation Networks

  • Luckily, the trust vectort, if computed in this manner, converges to the same thing for every peer!

  • Therefore, each peer doesn’t have to store and compute its own trust vector. The whole network can cooperate to store and compute t.


Non distributed algorithm l.jpg
Non-distributed Algorithm Networks

  • Initialize:

  • Repeat until convergence:


Distributed algorithm l.jpg

. Networks1

.5

0

0

0

.2

.1

.3

.2

.3

.1

.1

0 .2 0 .3 0 .5 .1 0 0 0

.2

Distributed Algorithm

  • No central authority to store and compute t.

  • Each peer i holds its own opinions ci.

  • For now, let’s ignore questions of lying, and let each peer store and compute its own trust value.


Distributed algorithm20 l.jpg
Distributed Algorithm Networks

For each peer i {

-First, ask peers who know you for their opinions of you.

-Repeat until convergence {

-Compute current trust value: ti(k+1) = c1jt1(k) +…+ cnjtn(k)

-Send your opinion cij and trust value ti(k)to your

acquaintances.

-Wait for the peers who know you to send you their trust

values and opinions.

}

}




Pre trusted peers l.jpg
Pre-trusted Peers Networks

  • Battling Malicious Collectives

  • Inactive Peers

  • Incorporating heuristic notions of trust

  • Convergence Rate


Pre trusted peers24 l.jpg
Pre-trusted Peers Networks

  • Battling Malicious Collectives

  • Inactive Peers

  • Incorporating heuristic notions of trust

  • Convergence Rate


Secure score management l.jpg

M Networks

?

?

M

M

M

?

?

Secure Score Management

  • Two basic ideas:

    • Instead of having a peer compute and store its own score, have another peer compute and store its score.

    • Have multiple score managers who vote on a peer’s score.

Score Manager

Distributed Hash Table

Score Managers


How to use the trust values t i l.jpg
How to use the trust values Networksti

  • When you get responses from multiple peers:

    • Deterministic: Choose the one with highest trust value.

    • Probabilistic: Choose a peer with probability proportional to its trust value.


Load distribution l.jpg
Load Distribution Networks

Probabilistic Download Choice

Deterministic Download Choice


Threat scenarios l.jpg
Threat Scenarios Networks

  • Malicious Individuals

    • Always provide inauthentic files.

  • Malicious Collective

    • Always provide inauthentic files.

    • Know each other. Give each other good opinions, and give other peers bad opinions.


More threat scenarios l.jpg
More Threat Scenarios Networks

  • Camouflaged Collective

    • Provide authentic files some of the time to trick good peers into giving them good opinions.

  • Malicious Spies

    • Some members of the collective give good files all the time, but give good opinions to malicious peers.





Malicious spies l.jpg
Malicious Spies Networks


Simulations l.jpg
Simulations Networks


Conclusion l.jpg
Conclusion Networks

  • Eigentrust

    • Dramatically reduces number of inauthentic files on the network.

    • Robust to malicious peers.

    • Low overhead.


The end l.jpg
The End Networks

  • Paper available at http://www.stanford.edu/~sdkamvar/research.html


Revised non distributed algorithm l.jpg
Revised Non-distributed Algorithm Networks

  • Initialize:

  • Repeat until convergence:


Simulator l.jpg
Simulator Networks

  • Query-Cycle Simulator

  • Models Peer Behavior.

    • Query Behavior, Uptime, Activity, Authenticity

    • Interests (Content Categories)

  • Models Peer Content

    • Assigns files to each peer based on interests, volume.

Schlosser, Condie, and Kamvar, “Simulating a File-Sharing P2P Network” 1st SemPGRID Workshop, 2003

(http://dbpubs.stanford.edu/pub/2003-28)


Computation l.jpg
Computation Networks

  • How long does it take for the cows to come home?

Haveliwala and Kamvar, “The Second Eigenvalue of the Google Matrix” (http://dbpubs.stanford.edu/pub/2003-20)


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