1 © 2005, daniel schwabe. vinicius almendra – swpw – iswc05 real-world trust policies vinicius...

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© 2005, Daniel Schwabe. Vinicius Almendra – SWPW – ISWC05

Real-world trust policies

Vinicius AlmendraDaniel Schwabe

Dept. of Informatics, PUC-RioISWC’05

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Agenda

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

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Problem Statement

Scenario: collection of semantic web dataThrough exchange: P2P networks,

semantic social desktopsThrough web navigation: Piggy

Bank-like approaches Problem: is this information

trustful?

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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

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Reliance

Reliance is NOT Blind Static Irrevocable

Reliance depends on Reasoning Circumstances Beliefs Freedom

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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

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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

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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

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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”

),'','',(),,'','('

)'',(),,,(

cPersontypeottrustedFaccsfriendmettrustedFac

friendpequalcopsP

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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’)

),'','',(),,'','('

)'',(),,,(

cPersontypeottrustedFaccsfriendmettrustedFac

friendpequalcopsP

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Trust Policies

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

),,,(),,,(),,,(

copsPcopsctassertedFacopsttrustedFac

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Trust Policies

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

),,,(),,,(),,,(

),,,(),,,(),,,(),,,(

321

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copsPcopsPcopsP

copsPcopsPcopsPcopsP

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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

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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).

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Asserted facts Trust policies Trusted factsI foaf:knows John (my context)

Self-trustContext infoGood news

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Asserted facts Trust policies Trusted factsI foaf:knows John (my context)


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Asserted facts Trust policies Trusted factsSelf-trustContext infoGood news

I foaf:knows John (my context)

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Asserted facts Trust policies Trusted factsemail_mary dc:creator Mary (email_mary)



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Asserted facts Trust policies Trusted factsemail_mary dc:creator Mary (email_mary)



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I foaf:knows John (my context)email_mary dc:creator Mary (email_mary)

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Asserted facts Trust policies Trusted factsFootball_news rdf:type news:News (email_mary)



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Football_news dc:description ‘Brazil World Cup champ’ (email_mary)’



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I foaf:knows mary (my context)



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Asserted facts Trust policies Trusted factsFootball_news dc:description ‘Brazil World Cup champ’ (email_mary)’




Football_news rdf:type news:News (email_mary)

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I foaf:knows John (my context)email_mary dc:creator Mary (email_mary)I foaf:knows mary (my context)Football_news rdf:type news:News (email_mary)Football_news dc:description ‘Brazil World Cup champ’ (email_mary)’

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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.

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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

1 © 2005, daniel schwabe. vinicius almendra – swpw – iswc05 real-world trust policies vinicius...

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