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Trust and Reputation System. S. Felix Wu University of California, Davis wu@cs.ucdavis.edu 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

Trust and Reputation System

S. Felix Wu

University of California, Davis

wu@cs.ucdavis.edu

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

Trust and Reputation System

occ tso 2pl
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
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
Cheating & Incentives
  • Selfish users in Gnutella and Bittorrent
  • eBay flaw seller ranking
  • Google page rank
  • Selfishness or Reputation boost

Trust and Reputation System

p2p trust model
P2P Trust Model
  • Less vulnerable?
  • Harder to implement? In a decentralized setting?

Trust and Reputation System

problem
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
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

some approaches
Some approaches
  • Past History
  • Friends of Friends
  • EigenTrust
  • PeerTrust
  • TrustDavis

Trust and Reputation System

terminology
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
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
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
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
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
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

friends of friends15
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

the math
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

problem with friends
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.

Trust and Reputation System

dual goal
Dual Goal
  • We want each peer to:
    • Know all peers.
    • Perform minimal computation (and storage).

Trust and Reputation System

knowing all peers
Knowing All Peers
  • Ask your friends: t=CTci.
  • Ask their friends: t=(CT)2ci.
  • Keep asking until the cows come home: t=(CT)nci.

Trust and Reputation System

minimal computation
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

non distributed algorithm
Non-distributed Algorithm
  • Initialize:
  • Repeat until convergence:

Trust and Reputation System

distributed algorithm
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.

Trust and Reputation System

distributed algorithm23
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.

}

}

Trust and Reputation System

probabilistic interpretation
Probabilistic Interpretation

Trust and Reputation System

malicious collectives
Malicious Collectives

Trust and Reputation System

pre trusted peers
Pre-trusted Peers
  • Battling Malicious Collectives
  • Inactive Peers
  • Incorporating heuristic notions of trust
  • Convergence Rate

Trust and Reputation System

pre trusted peers27
Pre-trusted Peers
  • Battling Malicious Collectives
  • Inactive Peers
  • Incorporating heuristic notions of trust
  • Convergence Rate

Trust and Reputation System

secure score management
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

Trust and Reputation System

peertrust system architecture
PeerTrust System Architecture

Trust Manager

Trust

Evaluation

Feedback

Submission

P1

Trust

Data

P6

P2

Data Locator

P2P Network

P2P Network

P5

P3

P4

Trust and Reputation System

how to use the trust values t i
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
Load Distribution

Probabilistic Download Choice

Deterministic Download Choice

Trust and Reputation System

threat scenarios
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
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.

Trust and Reputation System

malicious individuals
Malicious Individuals

Trust and Reputation System

malicious collective
Malicious Collective

Trust and Reputation System

camouflaged collective
Camouflaged Collective

Trust and Reputation System

p2p electronic communities
P2P Electronic Communities

Trust and Reputation System

motivation
Motivation

Trust and Reputation System

motivation39
Motivation
  • Should we buy?
  • How do we decide?

Trust and Reputation System

motivation40
Motivation

Trust and Reputation System

motivation41
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?

Trust and Reputation System

motivation42
Motivation
  • TrustDavis is a reputation system that realizes these goals.
  • It recasts these goals as the following properties:

Trust and Reputation System

motivation43
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
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
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
Motivation

$100

A

B

C

A Reference is:

Acceptance of Limited Liability.

Trust and Reputation System

motivation47
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
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

paying for references
Paying for References

50

150

100

50

150

Trust and Reputation System

paying for references50
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
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
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
Selling References

Trust and Reputation System

selling references54
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
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

selling references56
Selling References

Minimum Return/Risk Ration for Different Failure Probabilities

Cost/Insured Value – C/K

Insured Value as a fraction of total funds – f

Trust and Reputation System

a non exploitable strategy
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
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
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
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

a non exploitable strategy61
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
Outline
  • Motivation
  • The Model
    • Buying references
    • Selling references
  • A Non-Exploitable Strategy
  • Future Work
  • Conclusion
    • Key ideas

Trust and Reputation System

future work
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
Outline
  • Motivation
  • The Model
    • Buying references
    • Selling references
  • A Non-Exploitable Strategy
  • Future Work
  • Conclusion
    • Key ideas

Trust and Reputation System

conclusion
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

conclusion66
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
The End

Questions?

Thank you!

{defigueiredo,etbarr}@ucdavis.edu

Trust and Reputation System