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Trust and Reputation System. S. Felix Wu University of California, Davis [email protected] http://www.cs.ucdavis.edu/~wu/. OCC, TSO, 2PL. T1 r X T1 r Y T1 w X T1 r Z T1 w Y. Trust in P2P. The Service Provider provides a management system for trust and reputation Google’s “PageRank”

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Trust and reputation system l.jpg

Trust and Reputation System

S. Felix Wu

University of California, Davis

[email protected]

http://www.cs.ucdavis.edu/~wu/

Trust and Reputation System


Occ tso 2pl l.jpg
OCC, TSO, 2PL

  • T1 r X

  • T1 r Y

  • T1 w X

  • T1 r Z

  • T1 w Y

Trust and Reputation System


Trust in p2p l.jpg
Trust in P2P

  • The Service Provider provides a management system for trust and reputation

    • Google’s “PageRank”

    • Antivirus system

    • eBay’s seller reputation system

    • PKI

  • P2P -- everything hopefully to be P2P

    • Decentralized model for trust

Trust and Reputation System


Cheating incentives l.jpg
Cheating & Incentives

  • Selfish users in Gnutella and Bittorrent

  • eBay flaw seller ranking

  • Google page rank

  • Selfishness or Reputation boost

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P2P Trust Model

  • Less vulnerable?

  • Harder to implement? In a decentralized setting?

Trust and Reputation System


Problem l.jpg
Problem

  • 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

Trust and Reputation System


Problem7 l.jpg
Problem

0.9

0.1

  • 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.

Trust and Reputation System


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Some approaches

  • Past History

  • Friends of Friends

  • EigenTrust

  • PeerTrust

  • TrustDavis

Trust and Reputation System


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Terminology

t3=.5

C12=0.3

C23=0.7

t1=.3

C21=0.6

t2=.2

C14=0.01

t4=0

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

Trust and Reputation System

Peer 4


Local trust values l.jpg
Local Trust Values

  • 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.

Cij=

Peer i

Peer j

Trust and Reputation System


Normalizing local trust values l.jpg
Normalizing Local Trust Values

Peer 1

C12=0.9

Peer 2

C14=0.1

Peer 4

Peer 4

Peer 2

Peer 1

  • All cij non-negative

  • ci1 + ci2 + . . . + cin = 1

Trust and Reputation System


Local trust vector l.jpg
Local Trust Vector

Peer 1

C12=0.9

C14=0.1

Peer 2

Peer 4

Peer 4

c1

Peer 2

Peer 1

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

Trust and Reputation System


Past history l.jpg
Past history

?

?

?

?

?

Peer 6

?

Peer 4

Peer 1

  • 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.

Trust and Reputation System


Friends of friends l.jpg
Friends of Friends

Peer 6

Peer 4

Peer 1

Peer 2

Peer 8

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

Trust and Reputation System


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Friends of Friends

Peer 4

Peer 1

Peer 2

Peer 8

Peer 4

  • Weight their opinions by your trust in them.

Trust and Reputation System


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The Math

What they think of peer k.

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

Trust and Reputation System


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Problem with Friends

  • 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.

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Dual Goal

  • We want each peer to:

    • Know all peers.

    • Perform minimal computation (and storage).

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Knowing All Peers

  • Ask your friends: t=CTci.

  • Ask their friends: t=(CT)2ci.

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

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Minimal Computation

  • 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.

Trust and Reputation System


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Non-distributed Algorithm

  • Initialize:

  • Repeat until convergence:

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Distributed Algorithm

.1

.5

0

0

0

.2

.1

.3

.2

.3

.1

.1

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

.2

  • 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.

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Distributed Algorithm

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.

}

}

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Probabilistic Interpretation

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Malicious Collectives

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Pre-trusted Peers

  • Battling Malicious Collectives

  • Inactive Peers

  • Incorporating heuristic notions of trust

  • Convergence Rate

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Pre-trusted Peers

  • Battling Malicious Collectives

  • Inactive Peers

  • Incorporating heuristic notions of trust

  • Convergence Rate

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Secure Score Management

M

?

?

M

M

M

?

?

  • 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

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Peertrust system architecture l.jpg
PeerTrust System Architecture

Trust Manager

Trust

Evaluation

Feedback

Submission

P1

Trust

Data

P6

P2

Data Locator

P2P Network

P2P Network

P5

P3

P4

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

  • 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.

Trust and Reputation System


Load distribution l.jpg
Load Distribution

Probabilistic Download Choice

Deterministic Download Choice

Trust and Reputation System


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Threat Scenarios

  • 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.

Trust and Reputation System


More threat scenarios l.jpg
More Threat Scenarios

  • 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.

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Malicious Individuals

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Malicious Collective

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Camouflaged Collective

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P2P Electronic Communities

Trust and Reputation System


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Motivation

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Motivation

  • Should we buy?

  • How do we decide?

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Motivation

Trust and Reputation System


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Motivation

  • Should we buy?

  • How do we decide?

  • What we want:

    • accurately estimate risk of default

    • minimize the risk of default

    • minimize losses due to pseudonym change

    • avoid trusting a centralized authority

  • How do we achieve these goals?

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Motivation42 l.jpg
Motivation

  • TrustDavis is a reputation system that realizes these goals.

  • It recasts these goals as the following properties:

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Motivation43 l.jpg
Motivation

  • Agents can accurately estimate risk

    • Third parties provide accurate ratings

  • Honest buyer/seller avoids risk (if possible)

    • Insure transactions

  • No advantage in obtaining multiple identities

    • Agents can cope with pseudonym change

  • No need to trust a centralized authority

    • No centralized services needed

Trust and Reputation System


Motivation44 l.jpg
Motivation

Incentive Compatibility:

Each player should have incentives to perform the actions that enable the system to achieve a desired global outcome.

Trust and Reputation System


Motivation45 l.jpg
Motivation

  • Agents can accurately estimate risk

    • Third parties provide accurate ratings

  • Honest buyer/seller avoids risk (if possible)

    • Insure transactions

  • No advantage in obtaining multiple identities

    • Agents can cope with pseudonym change

  • No need to trust a centralized authority

    • No centralized services needed

      Incentive Compatibility!

Trust and Reputation System


Motivation46 l.jpg
Motivation

$100

A

B

C

A Reference is:

Acceptance of Limited Liability.

Trust and Reputation System


Motivation47 l.jpg
Motivation

  • Agents can accurately estimate risk

    • Third parties provide accurate ratings

    • Parties are liable for the references they provide

  • Honest buyer/seller avoids risk (if possible)

    • Insure transactions

    • Buyers/sellers pay for references to insure their transactions

  • No advantage in obtaining multiple identities

    • Agents can cope with pseudonym change

    • References are issued only to trusted identities

  • No need to trust a centralized authority

    • No centralized services needed

    • Anyone can issue a reference

      Use References!

Trust and Reputation System


Outline l.jpg
Outline

  • TrustDavis leverages social networks

  • For now, examples assume No False Claims (NFC)

  • The use of TrustDavis does NOT preclude trade outside the system.

Trust and Reputation System


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Paying for References

50

150

100

50

150

Trust and Reputation System


Paying for references50 l.jpg
Paying for References

$100 each

Trust-me.com

Blowout SALE!

$50 each!

$150!

How much is vb willing to pay to insure the transaction? (No riskless profitable arbitrage criterion)

Example:

  • vb wants to buy three shirts.

  • Shirts cost $100 each from a trustworthy seller

  • Unknown seller offers shirts for $50 each (but maybe they are only worth $25).

  • vb would risk 3 x $50 = $150 in the transaction

  • vb can borrow and lend money at rate r=1.25 through the period of the transaction

    For $30, vb can insure herself!

Trust and Reputation System


Paying for references51 l.jpg
Paying for References

To insure herself vb buys the shirts and a hedging portfolio as follows:

  • Instead of buying 3 shirts for $50 each she buys only 2, saving $50.

  • The buyer, vb , adds $30 of her own money and lends the resulting $80 at rate r = 1.25.

Trust and Reputation System


Paying for references52 l.jpg
Paying for References

On Success:

  • vb obtains $100 from the loan and buysthe 3rd shirt

    On failure:

  • vb sells the two shirts for $25 each

  • gets $100 from the loan.

  • She obtains a total of $150

    Thus, vb can insure herself for $30.

Trust and Reputation System


Selling references l.jpg
Selling References

Trust and Reputation System


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Selling References

Seen as an investment…

On Success the ROI is:

On failure the ROI is:

If repeated many times the insurer may go bankrupt. Assume the insurer has W dollars available to insure this transaction.

Trust and Reputation System


Selling references55 l.jpg
Selling References

Insurer maximizes the expected value of the growth rate of capital (Kelly Criterion).

For given:

  • probability of failure p,

  • a desired growth rate of capital R; and,

  • fraction of the total funds W being risked in a transaction.

    The insurer can obtain a lower bound on the premium C.

Trust and Reputation System


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Selling References

Minimum Return/Risk Ration for Different Failure Probabilities

Cost/Insured Value – C/K

Insured Value as a fraction of total funds – f

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A non exploitable strategy l.jpg
A Non-Exploitable Strategy

Two Scenarios:

  • No False Claims - NFC

  • With False Claims - FC

    False claims only change the probability p.

    We can incorporate the cost of verification.

    Key Idea:

    Save part of the money obtained in successful transactions in excess of the opportunity cost.

Trust and Reputation System


A non exploitable strategy58 l.jpg
A Non-Exploitable Strategy

Example.

The buyer, vb, has $190 to spend on 1 of 3 options:

  • Buying 3 shirts from an unknown seller for $50 each and insuring the transaction for $40. She values each shirt at $100.

  • Buying 2 pairs of shoes from a reliable retailer for $70 each. She thinks each pair is worth $90.

  • Buying 1 game console for $150, from a reliable online shop. She values the console at $240.

Trust and Reputation System


A non exploitable strategy59 l.jpg
A Non-Exploitable Strategy

vb’s valuation for each of the 3 options is:

  • Shirts: 100 x 3 + 0 (no cash leftover) = $300

  • Pairs of Shoes: 90 x 2 + 50 (cash) = $230

  • Console: 240 x 1 + 40 (cash) = $280

    Gains in excess of the opportunity cost are:300-280=$20.

    Part of these $20 should be saved to insure future transactions.

Trust and Reputation System


A non exploitable strategy60 l.jpg
A Non-Exploitable Strategy

The Strategy:

  • Initially only provide references to known agents or those that leave a security deposit.

  • Insure all trade through references provided by trusted agents.

  • Do not provide more insurance than you can recover. Charge at least the lower bound for providing a reference.

  • Save part of the money received “in excess of the opportunity cost”.

Trust and Reputation System


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A Non-Exploitable Strategy

50

50

150

100

50

150

10

OK!

$10 saved to provide future insurance

Failed!

Payment made automatically by v1

Trust and Reputation System


Outline62 l.jpg
Outline

  • Motivation

  • The Model

    • Buying references

    • Selling references

  • A Non-Exploitable Strategy

  • Future Work

  • Conclusion

    • Key ideas

Trust and Reputation System


Future work l.jpg
Future Work

  • Simulation

    • sensitivity to estimates of p

    • growth rate of capital

    • dynamic behavior

  • Price Negotiation

    • should avoid “double spending” problem

    • fair distribution among insurers of the premium paid

Trust and Reputation System


Outline64 l.jpg
Outline

  • Motivation

  • The Model

    • Buying references

    • Selling references

  • A Non-Exploitable Strategy

  • Future Work

  • Conclusion

    • Key ideas

Trust and Reputation System


Conclusion l.jpg
Conclusion

TrustDavis provides:

  • Accurate Ratings

  • Non-exploitable strategy for honest agents

  • Pseudonym change tolerance

  • Decentralized infrastructure

    Through the use of References.

Trust and Reputation System


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Conclusion

Key Ideas:

  • Incentive Compatibility

    • Incentive to accurately rate

    • Incentive to insure

    • No incentive to change pseudonym

  • Saving gains in excess of the opportunity cost to insure future transactions.

Trust and Reputation System


The end l.jpg
The End

Questions?

Thank you!

{defigueiredo,etbarr}@ucdavis.edu

Trust and Reputation System


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