A Social Network-Based Trust Model for the Semantic Web Yu 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

Introduction Trust 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 effectively aspirinheadache cure subjectobjectpredicate

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 Model Easy-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 Q Di: # of steps between P and Q Wi: weight of the i-th path Mi: 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

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