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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
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Outline
Introduction
Related Work
Trust Model
Basic Definitions
Basic Mechanisms
Conclusion
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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
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Introduction
contribution
increase efficiency
evaluate trust from two dimensions
exploit formulas in probability and statistics
provide an algorithm to compute trust values simultaneously
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Related Work
Small World - Milgram’s experiment(1960s)
FilmTrust
EigenTrust algorithm
a reputation management algorithm for P2P networks
focus on security problems
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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
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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
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Example: Neighbor and Friends
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Basic Mechanisms
Local Database Storage
Trust Report Mechanism
Routine Report
Update Report
On-demand Report
Pull Mode and Push Mode
Honor Roll and Blacklist
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Local Database Storage
• Linked List
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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
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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
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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
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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
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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
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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
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Similarity of Preference
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Similarity of Preference
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Similarity of Preference
• A and B are similar
• A and C are not similar
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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