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In the Name of AllahIn the Name of Allah
Data and NetworkData and Network
Security Lab. (DNSL)Security Lab. (DNSL)
Sharif UniversitySharif University
of Technologyof Technology Security Lab. (DNSL)Security Lab. (DNSL)of Technologyof Technology
Sadegh Dorri Nogoorani, Rasool Jalili
Uncertainty in Probabilistic Trust ModelsUncertainty in Probabilistic Trust Models
The 26th IEEE Int. Conf. Advanced Information Networking and Applications (AINA 2012)
Sharif University of Technology, Tehran, I.R. IRAN
http://ce.sharif.edu/~dorri
Who Knows on the Net...?Who Knows on the Net...?
A notion of trust similar toA notion of trust similar to
real world trust isreal world trust is
2
real world trust isreal world trust is
needed in the virtualneeded in the virtual
world…world…
Coordinating AgentCoordinating Agent
Interactions withoutInteractions without
Strict ControlStrict Control
MechanismsMechanismsMechanismsMechanisms
Fig. by Peter Steiner (The New Yorker, 5 July 1993)
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
Uncertainty and TrustUncertainty and Trust
Uncertainty = Lack of InformationUncertainty = Lack of Information
Randomness, fuzziness, vagueness, ambiguity
3
Randomness, fuzziness, vagueness, ambiguity
Modeling of UncertaintyModeling of Uncertainty
Probability: a long history, suited to random events
Fuzzy sets: since 1960s, suitable for human interaction
Dempster-Shafer theory: since 1970s, based on beliefs
Uncertainty in TrustUncertainty in Trust
Uncertainty of information (error, human feedback, …)Uncertainty of information (error, human feedback, …)
Expiration of information: change in trustee behavior
Credibility of trust information: recommendations, path length
Trust modes: induction, abstraction, …
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
OutlineOutline
BackgroundBackground
Probabilistic trust framework
4
Probabilistic trust framework
Case StudyCase Study
Uncertainty in two prob. trust models
A ProposalA Proposal
Uncertainty-driven risk reduction with trustUncertainty-driven risk reduction with trust
Future WorkFuture Work
Other forms of uncertainty
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
BackgroundBackground5
Probabilistic Trust FrameworkProbabilistic Trust Framework
BackgroundBackground5
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
Trust ScenarioTrust Scenario
Direct Trust
6
Trustor Trustee
Direct Trust
Functional
Referential Functional
Functional
Indirect Trust (Inference)
Referential Functional
Recommenders
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
Probabilistic Trust FrameworkProbabilistic Trust Framework
Definition of Trust (Adopted from [GamDefinition of Trust (Adopted from [Gam9090]):]):
The subjective probability by which trustor expects that
7
The subjective probability by which trustor expects that
trustee performs a given action, on which its welfare
depends.
Trust: The (Expected) Probability of Positive OutcomeTrust: The (Expected) Probability of Positive Outcome
Action outcome:
Probability of success
Trust (Bayesian view)
},{ xxR =
),,|Pr( ,,
1
,, tetr
tn
tetr
t
tetr
t
tetr
t OOxOp …==
][ ,, tetrtetr
pE=τTrust (Bayesian view)
From now on, a specificFrom now on, a specific trtr,, tete, and, and tt are implicitlyare implicitly
assumed.assumed.
][ ,, tetr
t
tetr
t pE=τ
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
The Beta Trust Model [JIThe Beta Trust Model [JI0202]]
Outcomes Assumed to be Bernoulli Trials (Outcomes Assumed to be Bernoulli Trials (i.i.di.i.d.).)
 =
==
xop
8
Hence,Hence, pp Follows the Beta DistributionFollows the Beta Distribution
r: success outcomes
s: failure outcomes
Trust 1
]E[
+
==
r
pτ



=−
=
==
xop
xop
oO
1
)Pr(
sr
pp
sr
sr
pf )1(
)1()1(
)2(
)( −
+Γ+Γ
++Γ
=
Trust
Change of Trustee BehaviorChange of Trustee Behavior
Forgetting facotr (λ)
2
]E[
++
==
sr
pτ
)(
)(
}{)1(
}{)1(
tnxnttn
tnxnttn
OIss
OIrr
+=
+=
−
−
λ
λ
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
The HMM Trust Model [ESNThe HMM Trust Model [ESN1010]]
Trustee Is Modeled with aTrustee Is Modeled with a 22--State Hidden Markov ModelState Hidden Markov Model
(HMM)(HMM) –– ΩΩ
2 (hidden) states (benevolent/malicious)
9
2 (hidden) states (benevolent/malicious)
2 possible outputs with independent Bernoulli distributions in
each state.
Learning from HistoryLearning from History
The initial prob. of being in each state, transition between
states, and output distribution in each state
Using the Baum-Welch algorithm (expectation maximization)Using the Baum-Welch algorithm (expectation maximization)
Trust CalculationTrust Calculation
Probability of success: p
Trust: E[p] is calculated using the Forward-Backward algorithm.
)|Pr(
)|,Pr(
),|Pr(
Ω
Ω=
=Ω==
H
HxO
HxOp
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
Case StudyCase Study10
Uncertainty of the Beta and HMM ModelsUncertainty of the Beta and HMM Models
Case StudyCase Study10
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
Quantification of UncertaintyQuantification of Uncertainty
Confidence IntervalConfidence Interval
A well-known indicator of probabilistic uncertainty.
11
A well-known indicator of probabilistic uncertainty.
There is an almost general method to calculate them
(bootstrapping).
Is not bound to a specific uncertainty factor.
Definition:Definition:
Δτ = [τ1, τ2] is the δ confidence interval of τ if:
δτττ =≤≤ )Pr(
Example:Example: 00..9595 confidence interval of [confidence interval of [00..44,, 00..66]]
The real value is in [0.4,0.6] with probability 0.95
δτττ =≤≤ )Pr( 21
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
Real Trustee Simulation ModelReal Trustee Simulation Model
12
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili
Labels: b/m: benevolent/malicious, w/f: working, faulty
27 Mar. 2012
Average Confidence Interval LengthAverage Confidence Interval Length
(Varying(Varying ss))13
(n = 300 observations)
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
Average Confidence Interval LengthAverage Confidence Interval Length
(Varying(Varying nn))14
(Stability s = 0.4)
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
Summary of the Case StudiesSummary of the Case Studies
Great Amount of Uncertainty in Both ModelsGreat Amount of Uncertainty in Both Models
Especially with small history sizes (even with n = 100)
15
Especially with small history sizes (even with n = 100)
The Beta model is more certain with small ns
Improving CertaintyImproving Certainty
Beta: No way! Forgetting factor is a fixed setting.
HMM: Enriching history with more observationsHMM: Enriching history with more observations
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
A ProposalA Proposal16
UncertaintyUncertainty--Driven Risk ReductionDriven Risk Reduction
A ProposalA Proposal16
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
Risk and TrustRisk and Trust
17
Decision Risk = Uncertainty x CriticalityDecision Risk = Uncertainty x Criticality
Criticality: fixedCriticality: fixed
Uncertainty: can be reduced
LowMediumHigh
MediumHighHighHigh
Criticality
Uncertainty
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili
MediumMediumHighMedium
LowLowLowLow
Criticality
Decision RiskDecision Risk
27 Mar. 2012
UncertaintyUncertainty--Driven Risk ReductionDriven Risk Reduction
ExampleExample
Utility function 
 =+
=
xo
ou
units50
)(
18
Utility function
Utility and Its UncertaintyUtility and Its Uncertainty
Expected utility:
Uncertainty: (interval arith.)
A Random Simulation SampleA Random Simulation Sample
)().1()(. xuxuU ττ −+=
)().1()(. xuxuU ττ ∆−+∆=∆


=−
=
xo
ou
units20
)(
]67.0,29.0[48.0 bb
=∆=τ
A Random Simulation SampleA Random Simulation Sample
Beta and HMM trust:
Utility and uncertainty:
]60.0,43.0[52.0
]67.0,29.0[48.0
hh
bb
=∆=
=∆=
τ
τ
]06.22,15.10[35.16
]09.27,04.0[45.13
hh
bb
=∆=
−=∆=
U
U
U
U
Future Work and Open ProblemsFuture Work and Open Problems19 Future Work and Open ProblemsFuture Work and Open Problems19
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
Other Sources of UncertaintyOther Sources of Uncertainty
Model Uncertainty (Our Study)Model Uncertainty (Our Study)
Uncertainty caused by induction and abstraction
20
Uncertainty caused by induction and abstraction
Can consider significance of hypothesis testing
Uncertainty in ObservationsUncertainty in Observations
Monitoring Systems: random and systematic errors
Human Feedback: fuzziness and vagueness
Credibility of Information SourcesCredibility of Information Sources
Path-length in trust inferencePath-length in trust inference
Up-to-date information (time)
We Seek for an Integrated Model for All TheseWe Seek for an Integrated Model for All These
Uncertainty Types and SourcesUncertainty Types and Sources
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
ConclusionsConclusions
Uncertainty Is an Inherent Feature of TrustUncertainty Is an Inherent Feature of Trust
Uncertainty of trustee
21
Uncertainty of trustee
Uncertainty of models
Uncertainty Has Severe Effect on Existing ModelsUncertainty Has Severe Effect on Existing Models
Beta and HMM
Uncertainty Can be Used in conjunction with TrustUncertainty Can be Used in conjunction with TrustUncertainty Can be Used in conjunction with TrustUncertainty Can be Used in conjunction with Trust
InformationInformation
In decision-making and consideration of risk
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
Thanks!Thanks!22
My HomepageMy Homepage
http://ce.sharif.edu/~dorrihttp://ce.sharif.edu/~dorri
Thanks!Thanks!22
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
ReferencesReferences
[ESN[ESN1010] E.] E. ElSalamounyElSalamouny, V., V. SassoneSassone, and M. Nielsen,, and M. Nielsen,
“HMM“HMM--based trust model,” Formal Aspects in Securitybased trust model,” Formal Aspects in Security
23
“HMM“HMM--based trust model,” Formal Aspects in Securitybased trust model,” Formal Aspects in Security
and Trust, vol.and Trust, vol. 59835983, pp., pp. 2121--3535,, 20102010..
[Gam[Gam9090] D. Gambetta, “Can we trust] D. Gambetta, “Can we trust trusttrust,” in Trust:,” in Trust:
Making and breaking cooperative relations, Oxford,Making and breaking cooperative relations, Oxford,
UK: Basil Blackwell,UK: Basil Blackwell, 19901990, pp., pp. 213213––237237..
[JI[JI0202] A.] A. JøsangJøsang and R. Ismail, “The Beta Reputationand R. Ismail, “The Beta Reputation[JI[JI0202] A.] A. JøsangJøsang and R. Ismail, “The Beta Reputationand R. Ismail, “The Beta Reputation
System,” in Proceedings of theSystem,” in Proceedings of the 1515th Bled Conferenceth Bled Conference
on Electronic Commerce, Bled, Slovenia,on Electronic Commerce, Bled, Slovenia, 20022002..
Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012

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Uncertainty in Probabilistic Trust Models

  • 1. In the Name of AllahIn the Name of Allah Data and NetworkData and Network Security Lab. (DNSL)Security Lab. (DNSL) Sharif UniversitySharif University of Technologyof Technology Security Lab. (DNSL)Security Lab. (DNSL)of Technologyof Technology Sadegh Dorri Nogoorani, Rasool Jalili Uncertainty in Probabilistic Trust ModelsUncertainty in Probabilistic Trust Models The 26th IEEE Int. Conf. Advanced Information Networking and Applications (AINA 2012) Sharif University of Technology, Tehran, I.R. IRAN http://ce.sharif.edu/~dorri
  • 2. Who Knows on the Net...?Who Knows on the Net...? A notion of trust similar toA notion of trust similar to real world trust isreal world trust is 2 real world trust isreal world trust is needed in the virtualneeded in the virtual world…world… Coordinating AgentCoordinating Agent Interactions withoutInteractions without Strict ControlStrict Control MechanismsMechanismsMechanismsMechanisms Fig. by Peter Steiner (The New Yorker, 5 July 1993) Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 3. Uncertainty and TrustUncertainty and Trust Uncertainty = Lack of InformationUncertainty = Lack of Information Randomness, fuzziness, vagueness, ambiguity 3 Randomness, fuzziness, vagueness, ambiguity Modeling of UncertaintyModeling of Uncertainty Probability: a long history, suited to random events Fuzzy sets: since 1960s, suitable for human interaction Dempster-Shafer theory: since 1970s, based on beliefs Uncertainty in TrustUncertainty in Trust Uncertainty of information (error, human feedback, …)Uncertainty of information (error, human feedback, …) Expiration of information: change in trustee behavior Credibility of trust information: recommendations, path length Trust modes: induction, abstraction, … Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 4. OutlineOutline BackgroundBackground Probabilistic trust framework 4 Probabilistic trust framework Case StudyCase Study Uncertainty in two prob. trust models A ProposalA Proposal Uncertainty-driven risk reduction with trustUncertainty-driven risk reduction with trust Future WorkFuture Work Other forms of uncertainty Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 5. BackgroundBackground5 Probabilistic Trust FrameworkProbabilistic Trust Framework BackgroundBackground5 Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 6. Trust ScenarioTrust Scenario Direct Trust 6 Trustor Trustee Direct Trust Functional Referential Functional Functional Indirect Trust (Inference) Referential Functional Recommenders Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 7. Probabilistic Trust FrameworkProbabilistic Trust Framework Definition of Trust (Adopted from [GamDefinition of Trust (Adopted from [Gam9090]):]): The subjective probability by which trustor expects that 7 The subjective probability by which trustor expects that trustee performs a given action, on which its welfare depends. Trust: The (Expected) Probability of Positive OutcomeTrust: The (Expected) Probability of Positive Outcome Action outcome: Probability of success Trust (Bayesian view) },{ xxR = ),,|Pr( ,, 1 ,, tetr tn tetr t tetr t tetr t OOxOp …== ][ ,, tetrtetr pE=τTrust (Bayesian view) From now on, a specificFrom now on, a specific trtr,, tete, and, and tt are implicitlyare implicitly assumed.assumed. ][ ,, tetr t tetr t pE=τ Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 8. The Beta Trust Model [JIThe Beta Trust Model [JI0202]] Outcomes Assumed to be Bernoulli Trials (Outcomes Assumed to be Bernoulli Trials (i.i.di.i.d.).)  = == xop 8 Hence,Hence, pp Follows the Beta DistributionFollows the Beta Distribution r: success outcomes s: failure outcomes Trust 1 ]E[ + == r pτ    =− = == xop xop oO 1 )Pr( sr pp sr sr pf )1( )1()1( )2( )( − +Γ+Γ ++Γ = Trust Change of Trustee BehaviorChange of Trustee Behavior Forgetting facotr (λ) 2 ]E[ ++ == sr pτ )( )( }{)1( }{)1( tnxnttn tnxnttn OIss OIrr += += − − λ λ Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 9. The HMM Trust Model [ESNThe HMM Trust Model [ESN1010]] Trustee Is Modeled with aTrustee Is Modeled with a 22--State Hidden Markov ModelState Hidden Markov Model (HMM)(HMM) –– ΩΩ 2 (hidden) states (benevolent/malicious) 9 2 (hidden) states (benevolent/malicious) 2 possible outputs with independent Bernoulli distributions in each state. Learning from HistoryLearning from History The initial prob. of being in each state, transition between states, and output distribution in each state Using the Baum-Welch algorithm (expectation maximization)Using the Baum-Welch algorithm (expectation maximization) Trust CalculationTrust Calculation Probability of success: p Trust: E[p] is calculated using the Forward-Backward algorithm. )|Pr( )|,Pr( ),|Pr( Ω Ω= =Ω== H HxO HxOp Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 10. Case StudyCase Study10 Uncertainty of the Beta and HMM ModelsUncertainty of the Beta and HMM Models Case StudyCase Study10 Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 11. Quantification of UncertaintyQuantification of Uncertainty Confidence IntervalConfidence Interval A well-known indicator of probabilistic uncertainty. 11 A well-known indicator of probabilistic uncertainty. There is an almost general method to calculate them (bootstrapping). Is not bound to a specific uncertainty factor. Definition:Definition: Δτ = [τ1, τ2] is the δ confidence interval of τ if: δτττ =≤≤ )Pr( Example:Example: 00..9595 confidence interval of [confidence interval of [00..44,, 00..66]] The real value is in [0.4,0.6] with probability 0.95 δτττ =≤≤ )Pr( 21 Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 12. Real Trustee Simulation ModelReal Trustee Simulation Model 12 Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili Labels: b/m: benevolent/malicious, w/f: working, faulty 27 Mar. 2012
  • 13. Average Confidence Interval LengthAverage Confidence Interval Length (Varying(Varying ss))13 (n = 300 observations) Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 14. Average Confidence Interval LengthAverage Confidence Interval Length (Varying(Varying nn))14 (Stability s = 0.4) Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 15. Summary of the Case StudiesSummary of the Case Studies Great Amount of Uncertainty in Both ModelsGreat Amount of Uncertainty in Both Models Especially with small history sizes (even with n = 100) 15 Especially with small history sizes (even with n = 100) The Beta model is more certain with small ns Improving CertaintyImproving Certainty Beta: No way! Forgetting factor is a fixed setting. HMM: Enriching history with more observationsHMM: Enriching history with more observations Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 16. A ProposalA Proposal16 UncertaintyUncertainty--Driven Risk ReductionDriven Risk Reduction A ProposalA Proposal16 Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 17. Risk and TrustRisk and Trust 17 Decision Risk = Uncertainty x CriticalityDecision Risk = Uncertainty x Criticality Criticality: fixedCriticality: fixed Uncertainty: can be reduced LowMediumHigh MediumHighHighHigh Criticality Uncertainty Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili MediumMediumHighMedium LowLowLowLow Criticality Decision RiskDecision Risk 27 Mar. 2012
  • 18. UncertaintyUncertainty--Driven Risk ReductionDriven Risk Reduction ExampleExample Utility function   =+ = xo ou units50 )( 18 Utility function Utility and Its UncertaintyUtility and Its Uncertainty Expected utility: Uncertainty: (interval arith.) A Random Simulation SampleA Random Simulation Sample )().1()(. xuxuU ττ −+= )().1()(. xuxuU ττ ∆−+∆=∆   =− = xo ou units20 )( ]67.0,29.0[48.0 bb =∆=τ A Random Simulation SampleA Random Simulation Sample Beta and HMM trust: Utility and uncertainty: ]60.0,43.0[52.0 ]67.0,29.0[48.0 hh bb =∆= =∆= τ τ ]06.22,15.10[35.16 ]09.27,04.0[45.13 hh bb =∆= −=∆= U U U U
  • 19. Future Work and Open ProblemsFuture Work and Open Problems19 Future Work and Open ProblemsFuture Work and Open Problems19 Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 20. Other Sources of UncertaintyOther Sources of Uncertainty Model Uncertainty (Our Study)Model Uncertainty (Our Study) Uncertainty caused by induction and abstraction 20 Uncertainty caused by induction and abstraction Can consider significance of hypothesis testing Uncertainty in ObservationsUncertainty in Observations Monitoring Systems: random and systematic errors Human Feedback: fuzziness and vagueness Credibility of Information SourcesCredibility of Information Sources Path-length in trust inferencePath-length in trust inference Up-to-date information (time) We Seek for an Integrated Model for All TheseWe Seek for an Integrated Model for All These Uncertainty Types and SourcesUncertainty Types and Sources Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 21. ConclusionsConclusions Uncertainty Is an Inherent Feature of TrustUncertainty Is an Inherent Feature of Trust Uncertainty of trustee 21 Uncertainty of trustee Uncertainty of models Uncertainty Has Severe Effect on Existing ModelsUncertainty Has Severe Effect on Existing Models Beta and HMM Uncertainty Can be Used in conjunction with TrustUncertainty Can be Used in conjunction with TrustUncertainty Can be Used in conjunction with TrustUncertainty Can be Used in conjunction with Trust InformationInformation In decision-making and consideration of risk Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012
  • 23. ReferencesReferences [ESN[ESN1010] E.] E. ElSalamounyElSalamouny, V., V. SassoneSassone, and M. Nielsen,, and M. Nielsen, “HMM“HMM--based trust model,” Formal Aspects in Securitybased trust model,” Formal Aspects in Security 23 “HMM“HMM--based trust model,” Formal Aspects in Securitybased trust model,” Formal Aspects in Security and Trust, vol.and Trust, vol. 59835983, pp., pp. 2121--3535,, 20102010.. [Gam[Gam9090] D. Gambetta, “Can we trust] D. Gambetta, “Can we trust trusttrust,” in Trust:,” in Trust: Making and breaking cooperative relations, Oxford,Making and breaking cooperative relations, Oxford, UK: Basil Blackwell,UK: Basil Blackwell, 19901990, pp., pp. 213213––237237.. [JI[JI0202] A.] A. JøsangJøsang and R. Ismail, “The Beta Reputationand R. Ismail, “The Beta Reputation[JI[JI0202] A.] A. JøsangJøsang and R. Ismail, “The Beta Reputationand R. Ismail, “The Beta Reputation System,” in Proceedings of theSystem,” in Proceedings of the 1515th Bled Conferenceth Bled Conference on Electronic Commerce, Bled, Slovenia,on Electronic Commerce, Bled, Slovenia, 20022002.. Uncertainty in Trust Models, Sadegh Dorri N., Rasool Jalili 27 Mar. 2012