ITEC 810 Workshop Paper: A Survey of Web-based Social Network Trust Student: Eric Wang 41176774 Supervisor: Yan Wang Subject Coordinator: Robert Dale 5th Jun 2009

Agenda Trust and WBSN Trust inference in a WBSN Survey of trust inference mechanisms Findings and discussion Possible area for future studies Conclusion Question time Slide 2

Problem How trustworthy is the opinion of a friend of my friend who I do not have direct relationship with? How to infer trust on people I do not have direct relationship via the people I do have direct relationship? How to accurately estimate this inferred trust without negative impact from malicious users? Slide 3

Trust and WBSN Trust Web-based Social Network Have confidence or faith in a person or a piece of information Web-based Social Network MySpace, Facebook, LinkedIn Decisions are made based on trust Which movie should I watch next weekend? Should I buy Windows Vista or wait for Windows 7? How can we estimate trust from someone not directly connected to me in a WBSN? Slide 4

Properties of trust in a WBSN Asymmetry Personalisation Transitive Slide 5

Trust inference in a WBSN Trust inference in a trust diagram Slide 6

Survey of Trust Inference Mechanisms TidalTrust Binary Trust Algorithms Advogato Trust Metric Appleseed SocialTrust FuzzyTrust Algorithm SUNNY Trusted Gossip RN-Trust We select 5 to present here Slide 7

TidalTrust Jennifer Golbeck, 2005 Simple algorithm - scalable Generic – applicable to any WBSN Applies two conditions to increase accuracy Only consider the shortest path Trust threshold limit on highly trusted neighbours Most frequently cited and compared by others Slide 8

SocialTrust James Caverlee, et al, 2008 Distinguishing relationship quality from trust Incorporating personalised user feedback Three components: Current quality component History component Adaptation to change component More robust against malicious attacks than PageRank from Google Slide 9

FuzzyTrust Algorithm James Caverlee, et al, 2008 Fuzzy linguistic terms instead of numeric trust values Computes trust from stronger and shorter paths More meaningful information when there is contradictory information Slide 10

Trusted Gossip Arindam Mitra and Mucumaru Maheswaran, 2007 Encourages spread of good information while restrict spam messages Perform more than one step of trust estimation Bayesian trust estimation Recommender system Three approaches on where node-level and message level filtering applied Receiver Initiated Sender Initiated Using both as Hybrid Robust against reputation distribution Slide 11

RN-Trust Mohsen Taherian et al, 2008 Electrical resistance network model Simple and generic Scalable Improve on weakness of TidalTrust All paths considered, not only shortest paths Slide 12

Findings and discussion Approach of trust inference estimation Only trust values in the shortest or strongest paths Perform more than one step of trust estimation Include user feedback Fuzzy linguistic terms instead of numeric trust values Electrical resistance model Merits and Weaknesses Increased accuracy Simplicity Scalability Dynamic update by feedback Robust against reputation distribution Robust against malicious attacks Slide 13

Possible topics for future studies Further improve accuracy and precision Develop event based trust inference algorithm Develop context aware extensions Other applications of inference trust Military Intelligence Data mining Computing requirement CPU process Storage Network Slide 14

Conclusion Define trust and its properties in WBSN Survey of existing trust inference mechanisms Merits and weaknesses in existing trust inference mechanisms 5 possible topics for future studies Slide 15

Question Time Question from the audience Slide 16