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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.

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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 mean1
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 model1
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 policies1
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 policies2
Trust Policies

  • Trust axiom

    • Given a fact (s,p,o,c)

    • Given a trust policy P


Trust policies3
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 info1
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).















Implementation
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.


Conclusions and future work
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