Real world trust policies ISWC05

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Real-world trust policies:

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

Agenda:

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

Problem Statement:

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?

What Does Trust Mean?:

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

Reliance:

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

What Does Trust Mean?:

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

The Trust Model:

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

The Trust Model:

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

Trust policies:

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”

Trust Policies:

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’)

Trust Policies:

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

Trust Policies:

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

An Example – Trust in News Info:

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

An Example – Trust in News Info:

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).