REPUTATIONBASED TRUST MODELLING

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REPUTATION-BASED TRUST MODELLING

• Gayatri Swamynathan
• CS290F, 12/2/04

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OUTLINE

• Quick Overview
• Clarifications
• Project Changes
• The Model
• Performance Metrics
• Conclusions and Future Work

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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

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Trust The Notion of Context

• Trusting a peer to
• Provide good service (here, files)
• Provide good referrals/opinions
• Malicious (false positives/negatives)
• Incompatible viewpoints

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Some Project Changes

• Decentralized network (more generic)
• Not just a survey
• Implementing a trust model to understand the
  benefits of using reputation

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OUTLINE

• Quick Overview
• Clarifications
• Project Changes
• The Model
• Performance Metrics
• Conclusions and Future Work

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The Model File Transfer
Bootstrap File Holders
Random File Requests Generator
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List of File X Providers
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Peer requests file X
File Transfer
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Process Trust Values to choose the best peer
Post-Transaction update
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Local Trust Table
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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) gt serviceThreshold, interact!
• If no serviceTrust value known, trust strangers!
• opinionThreshold

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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) gt opinionThreshold
• Accept fh(k,j)
• Modify st(i,j)
• Add k to goodOps(j)/badOps(j)
• Case2
• If op(i,k) gt opinionThreshold , but st(i,j)
  fh(k,j)
• Do NotAccept fh(k,j)
• Modify op(i,k)

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The Model Transfer of Trust

• Case3
• If op(i,k) lt opinionThreshold
• Do Not Accept fh(k,j)
• Case4
• If op(i,k) lt 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 strangers
  opinions!

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The Model

• But wait
• Node i now interacts with Node j (i.e. st(i,j) gt
  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 !!

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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

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Simulation Setting Sample Topology

• Parameters
• Number of nodes
• Probability of an edge from a node

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OUTLINE

• Quick Overview
• Clarifications
• Project Changes
• The Model
• Performance Metrics
• Conclusions and Future Work

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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

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Simulation Setting 25-node network
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Performance Metrics Detecting Malicious Behavior
With and Without Reputation (threshold values
0.5)
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Performance Metrics Detecting Malicious Behavior
Different Tolerance thresholds (t0.25, t0.5)
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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