A Social Network-Based Trust Model for the Semantic

WebYu Zhang, Huajun Chen, and Zhaohui Wu

Grid Computing Lab, College of Computer Science, Zhejiang University

Speaker: Yi-Ching Huang

Outline

Introduction

Related Work

Trust Model

Basic Definitions

Basic Mechanisms

Conclusion

IntroductionTrust is essential to secure and high quality interactions on the Semantic Web

Semantic Web can be view as a collection of intelligent agent

RDF (Resource Description Framework)

machine-understandable

Example: aspirin can cure headache effectivelyaspirin headache

curesubjec

tobjectpredica

te

Introduction

contribution

increase efficiency

evaluate trust from two dimensions

exploit formulas in probability and statistics

provide an algorithm to compute trust values simultaneously

Related Work

Small World - Milgram’s experiment(1960s)

FilmTrust

EigenTrust algorithm

a reputation management algorithm for P2P networks

focus on security problems

Trust Model

Source: FOAF data

RDF/XML Semantic Web vocabulary

Easy to process and merge by machine

Allows users to specify who they know and build a web of acquaintances

Use a graph to describe a social structure

G = (V, E)

V: resources, E: predicates

Basic Definitions

Def. 1 : Trust Rating

which degree a consumer’s evaluation about a provider’s ability

Def. 2 : Reliable Factor

which degree that a consumer agent believes the trust information

Def. 3 : Neighbor

Def. 4 : Friend

Example: Neighbor and Friends

Basic Mechanisms

Local Database Storage

Trust Report Mechanism

Routine Report

Update Report

On-demand Report

Pull Mode and Push Mode

Honor Roll and Blacklist

Local Database Storage

• Linked List

Trust Report Mechanism

Problem: Semantic Web is “openness”

it is hard to know whether our past experience is valuable or meaningless

3 types

Routine Report

Update Report

On-demand Report

Pull Mode and Push Mode

Pull mode

when the consumer needs some trust information, it takes the initiative to “pull” trust news from its acquaintances

Push mode

the publisher pushes the trust information directly to the consumer

Honor Roll and Blacklist

• Problem: it consumes much time to calculate trust values and transfer information

• Want to speed up the process

• Solutions

• Honor Roll: behave well

• Blacklist: behave badly

• Both store in linked list in the local database

• Need to update

The Algorithm of the Trust ModelEasy-to-compute

Avoid client to wait the results

Parallel arithmetic

Use BFS to expands out from source to sink through the trust network

All the path compute trust values simultaneously

Two dimensions

Trust rating

Reliable factor

The Algorithm of the Trust Model

N: # of paths from P to QDi: # of steps between P and QWi: weight of the i-th pathMi: Q’s immediate friend or neighbor on the i-th path

Similarity of Preference

Probability and statistics theory

Assumption: the closer of each pair of trust ratings, the more similar of the two agents

Define

| E(x) | < 0.3 and S(x) < 0.1

Similarity of Preference

Similarity of Preference

Similarity of Preference

• A and B are similar

• A and C are not similar

Conclusion

the algorithm of the model is simple, efficient and flexible

the trust model does not provide a mechanism to deal with lying or betrayal of agents

plan to incorporate reasoning and learning abilities