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REPUTATION-BASED TRUST MODELLING. Gayatri Swamynathan CS290F, 12/2/04. OUTLINE. Quick Overview Clarifications Project Changes The Model Performance Metrics Conclusions and Future Work. OVERVIEW. Trust Management: any mechanism that helps establish trust (or distrust) between peers

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reputation based trust modelling

REPUTATION-BASED TRUST MODELLING

Gayatri Swamynathan

CS290F, 12/2/04

outline
OUTLINE
  • Quick Overview
    • Clarifications
    • Project Changes
  • The Model
  • Performance Metrics
  • Conclusions and Future Work
overview
OVERVIEW
  • Trust Management: any mechanism that helps establish trust (or distrust) between peers
  • Reputation is a measure that is derived from direct or indirect knowledge of earlier interactions of peers and is used to access the level of trust a peer puts into another
  • Reputation-based Trust Management : A Risk Management Technique
trust the notion of context
Trust: The Notion of Context
  • Trusting a peer to
    • Provide good service (here, files)
    • Provide good referrals/opinions
      • Malicious (false positives/negatives)
      • Incompatible viewpoints
some project changes
Some Project Changes
  • Decentralized network (more generic)
  • Not just a survey
    • Implementing a trust model to understand the benefits of using reputation
outline1
OUTLINE
  • Quick Overview
    • Clarifications
    • Project Changes
  • The Model
  • Performance Metrics
  • Conclusions and Future Work
the model file transfer
The Model: File Transfer

Bootstrap: File Holders

Random

File Requests Generator

2

List of File X Providers

1

3

Peer requests

file X

File Transfer

5

Process Trust Values to choose the best peer

Post-Transaction update

4

6

Local Trust Table

the model representing trust
The Model: Representing Trust
  • Data Structures to represent Trust
    • ServiceTrust (st)
    • opinionTrust (op)
    • firstHand (fh) to represent direct-interaction observations
  • Tolerance Thresholds
    • serviceThreshold
      • values lower than this indicate untrustworthiness
      • If st(i,j) > serviceThreshold, interact!
      • If no serviceTrust value known, trust strangers!
    • opinionThreshold
the model transfer of trust
The Model: Transfer of Trust

Node i receives fh(k,j) where:

firstHand information on Node j generated by Node k, post transaction

Case1:

If op(i,k) > opinionThreshold {

Accept fh(k,j)

Modify st(i,j)

Add k to goodOps(j)/badOps(j)

}

Case2:

If op(i,k) > opinionThreshold , but st(i,j)≈ fh(k,j)

Do NotAccept fh(k,j)

Modify op(i,k)

}

the model transfer of trust1
The Model: Transfer of Trust

Case3:

If op(i,k) < opinionThreshold {

Do Not Accept fh(k,j)

}

Case4:

If op(i,k) < opinionThreshold , but st(i,j)≈ fh(k,j)

Accept fh(k,j)

Modify op(i,k)

Add k to goodOps/badOps list

}

Case5:

No Opinion Values Known: trust stranger’s opinions!

the model
The Model

But wait…

Node i now interacts with Node j (i.e. st(i,j) > serviceThreshold)

If the interaction is bad,

Node i checks goodOps(j) and reduces opinionTrust values of all the nodes that gave a thumbs-up to Node j !!

simulation setting topology
Simulation Setting: Topology
  • Decentralized Network (10 nodes – 25 nodes)
  • Stanford GraphBase
    • Platform for general graph representation and manipulation
  • GT-ITM (Georgia Tech Internetwork Topology Model)
    • Creation and analysis of graph models of network topology
  • Implementation in NS2 and C++
    • Peer Agent
    • FTP Agent
simulation setting sample topology
Simulation Setting: Sample Topology
  • Parameters:
  • Number of nodes
  • Probability of an edge from a node
outline2
OUTLINE
  • Quick Overview
    • Clarifications
    • Project Changes
  • The Model
  • Performance Metrics
  • Conclusions and Future Work
performance metrics
Performance Metrics
  • Mean Time to detect malicious behavior
    • with and without reputation-based trust model
    • different tolerance thresholds
  • Overheads:
    • Storage
      • ~20 bytes per trust-table entry for a 10-node network !!
      • Timestamps?
    • Control Messages
      • UDP packets
performance metrics detecting malicious behavior with and without reputation threshold values 0 5
Performance Metrics: Detecting Malicious Behavior With and Without Reputation (threshold values = 0.5)
conclusions
Conclusions
  • Decentralized networks with Reputation-based trust mechanisms help systems work better.
  • Future Work
    • Post Transaction Analysis of Requesting-Peer
    • Collusion
    • Reputation-history similar to Credit History