1 / 29

Real-world trust policies

Real-world trust policies. Vinicius Almendra Daniel Schwabe Dept. of Informatics, PUC-Rio ISWC’05. Agenda. Problem Statement What Does Trust Mean? The Trust Model Building Real-world Trust Policies An Example Future Work Conclusions. Problem Statement.

kjohnston
Download Presentation

Real-world trust policies

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Real-world trust policies Vinicius Almendra Daniel Schwabe Dept. of Informatics, PUC-Rio ISWC’05

  2. Agenda • Problem Statement • What Does Trust Mean? • The Trust Model • Building Real-world Trust Policies • An Example • Future Work • Conclusions

  3. Problem Statement • Scenario: collection of semantic web data • Through exchange: P2P networks, semantic social desktops • Through web navigation: Piggy Bank-like approaches • Problem: is this information trustful?

  4. What Does Trust Mean? • Using a real-world model of trust: “trust is reliance on received information” (Gerck, 1998) • To trust someone or something => To rely on it to achieve some goal • Reliance on a banking Website to move money • Reliance on a car or plane while doing a trip • Reliance on a statistical software • Reliance implies an action (actual or future) – boolean value

  5. Reliance • Reliance is NOT • Blind • Static • Irrevocable • Reliance depends on • Reasoning • Circumstances • Beliefs • Freedom

  6. What Does Trust Mean? • Reliance is useful because • It gives a mental frame to think about trustfulness • It links trust with action, while keeping them apart • Why real-world trust? • The model is being built in order to support an easy mapping from daily trust decisions to a computable representation

  7. The Trust Model • To trust is to virtually rely • Trust is subjective: it depends on who trusts, the trusting agent • Object of trust: facts • Statements about reality • Facts can be just known (asserted) and can also be trusted. • Trust decision: happens when the trusting agent decides that an asserted fact can be trusted

  8. The Trust Model • Trust decision must be reasonable: there must be a justification for accepting that a fact is trustful • Justification is based on beliefs, which are grounded on trusted facts • A trust policy is a set of rules that the trust agent uses to deduce the trustfulness of a fact. It is associated with a goal • Trust policies should be built incrementally

  9. Trust policies • Answer the question: “is this fact trustful?” • Reasoning behind a trust decision can be expressed using classic logic • Trust policy = predicate over a fact asserting its trustfulness • Fact = (s,p,o,c) – subject, predicate, object and context • Reasoning about trusted facts • May use the domain theory of the agent • Example: “I trust that a person A is a friend of a person B when A is my friend and B is known to be a person”

  10. Trust Policies • If the facts below were trusted: • (‘Me’, ‘friend’, ‘John’, ‘My context’) • (‘Erick’, ‘type’, ‘Person’, ‘My context’) • This fact would be trusted • (‘John’, ‘friend’, ‘Erick’, ‘My context’) • But not these one • (‘Mary’, ‘friend’, ‘John’, ‘Mary’s context’) • (‘John’, ‘brother’, ‘Erick’, ‘Robert’s context’)

  11. Trust Policies • Trust axiom • Given a fact (s,p,o,c) • Given a trust policy P

  12. Trust Policies • Trust Policies can be combined through aggregation (union of trustful facts) or specialization (intersection of trusted facts)

  13. An Example – Trust in News Info • Scenario: a person looking for trustful news-related information • We start with three policies: • Self-trust: trust everything contained in “my” context • Context info: trust everything stated about a context • Good News: trust news that come from friends

  14. An Example – Trust in News Info • Policies described as Prolog clauses: trustedFact(S,P,O,C) :- assertedFact(S,P,O,C), goodNewsRelatedInfo(S,P,O,C). goodNewsRelatedInfo(S,P,O,C) :-selfTrust(S,P,O,C). goodNewsRelatedInfo(C,_,_,C). goodNewsRelatedInfo(S,P,O,C) :- goodNews(S,P,O,C). goodNews(_,rdf:type, 'news:News' ,C) :- trustedFact(C, dc:creator, Friend, _), trustedFact(myself, foaf:knows, Friend, my_context).

  15. An Example – Trust in News Info

  16. An Example – Trust in News Info

  17. An Example – Trust in News Info

  18. An Example – Trust in News Info

  19. An Example – Trust in News Info

  20. An Example – Trust in News Info

  21. An Example – Trust in News Info

  22. An Example – Trust in News Info

  23. An Example – Trust in News Info

  24. An Example – Trust in News Info

  25. An Example – Trust in News Info

  26. An Example – Trust in News Info

  27. An Example – Trust in News Info

  28. Implementation • A first implementation was done using named graphs • We moved to logic programs (XSB Prolog) to better represent trust policies • Next step: link these logic programs with a RDF triple store.

  29. Conclusions and Future Work • Simple approach promising • Ongoing work • Handling negation – could be pushed to the underlying KB • Adding support to inference – to take advantage of the domain knowledge • Linking with RDF triple stores • Providing a method to build trust policies that keeps “real-world” property • Build to help users specify policies • Apply to realistic case study

More Related