University of Westminster – Y. Zetuny, G. Terstyanszky, S. Winter, P. Kacsuk Centre for Parallel Computing Cavendish School of Informatics.

University of Westminster – www.cpc.wmin.ac.uk

Y. Zetuny, G. Terstyanszky, S. Winter, P. Kacsuk

Centre for Parallel ComputingCavendish School of Informatics

University of Westminster

Articulating Subjective Trust-Based Decision Strategies Utilizing the Reputation-Policy Trust Management Service

OVERVIEW Research Background Reputation-Policy Based Trust in Grid computing Reputation-Policy Trust Model Grid Reputation-Policy Trust Management Service

Architecture Test bed deployment, simulation & tests Summary

Trust Management

Approaches for trust management:– Static -> policy-based: Web services,

E-Commerce– Dynamic -> Reputation based: P2P, Ad-hoc

networks

Requirements for trust management:– Establishing dynamic trust evaluation of

resources to minimise risk of execution failure– Autonomic trust decision making based on

reputation evaluation strategy– Expressing reputation using policy assertions in

order to promote semantic interoperability.

Trust Management

Challenges of Trust Management Challenge No. 1:Traditional trust management in Grid computing

addresses trust through security policies.Solution:Reputation provides trust evaluation measurements in

dynamic scenarios between Grid actors and resources.Challenge No. 2:Grid actors are not able to calculate the trust value of a

Grid resource by specifying their own trust evaluation criteria and they are obliged to rely on a community reputation algorithm to compute trust values.

Solution:Combining policy framework with a reputation algorithm

and allowing Grid actors to be involved in the trust and reputation evaluation process

Distributed data model: trust data is divided between Grid client and reputation

algorithm.Trust Model contains three artefacts:

◦Trust Decision Strategy (TDS) > Heuristics Trust Evaluation Model > Subjective view Trust Decision Model > Opportunistic view

◦Opinion Matrices (OM) Store and make available historical execution data

◦Correlation Process (CP) Correlates each opinion element in the TDS with its

historical ratings in the OM. Computes trust values using an Opinion Summary

Table (OST).

Reputation-Based Trust Management

Trust Decision Strategy is represented by Fuzzy Tree Model (FTM) expressing reputation-policy statements which are defined by trusting agents.

It has two branches:– Trust Evaluation Model (TEM)

• Permutation of opinions representing subjective trust building blocks (e.g. availability, reliability, cost, etc).

– Trust Decision Model (TDM)• Potential trust value calculation outcomes and

opportunistic correspondent courses of actions.

Trust Decision Strategy

TDS = {TEM; (TDR1;TDR2; … ;TDRn)}

Trust Decision Strategy

Opinion Matrice

values are based on time series distribution, trust decay function, cut off time and weighted mean

When an execution is completed, a trusting agent evaluates the quality of the transaction on the resource and the opinion matrice stores these historical evaluation feedback values.

computed trust (fuzzy) value

MS - opinion matrice set

M(O) - opinion matrice (application or job-level)

Correlation Process (CP) It matches each opinion defined in TDS with its

historical references in the Opinion Matrices and calculating the trust value for that opinion.

Each TDS opinion type is routed via the Metrics Pool (MP) in order to return a correspondent OM.

The CP examines the opinion’s source nodes (experience, reputation) and their weight factors.

The CP generates two vectors: experience vector and reputation vector and calculates the opinion value using a standard mean:

GREPTrust’s domains:•Client Domain – Grid Client, TDS Data Store•Service Domain – Querying Manager, Feedback Manager

and Admin Manager•Data Domain – Reputation-Policy Data Store

Architecture of Reputation-Based Trust Management

Step No. 1– Grid client submits a Reputation-Policy Query (RPQ)

to the GREPTrust resource.

Step No. 2

- GREPTrust resource processes the RPQ, generates Reputation-Policy Report (RPR) and delivers it to the Grid client.

Step No. 3– The Grid client utilises the RPR in order to make a

decision on which resource(s) to submit the job to.

Steps of Reputation-Based Trust Management

STEP1: Process TDS Evaluation Model

STEP2: Process TDS Decision Model

STEP3: Generate Reputation-Policy Report

Processing a Trust Query

Trust value Trust level

INPUT OUTPUT

IF trust_value IS poor THEN trust_level IS noneIF trust_value IS good THEN trust_level IS limitedIF trust_value IS excellent THEN trust_level IS full

RULES

Trust valueIs interpreted as

{poor, good, excellent}

INPUT TERM

Trust levelIs assigned to be

{none, limited, full}

OUTPUT TERM

TDS – Fuzzy Interference Engine

<TrustEvaluationModel> <Opinions> <Opinion Type="1" Weight="0.1"> <Sources> <Source Type="Experience" Weight="0.9"/> <Source Type="Reputation" Weight="0.1"/> </Sources> </Opinion> <Opinion Type="2" Weight="0.9"> <Sources> <Source Type="Experience" Weight="0.9"/> <Source Type="Reputation" Weight="0.1"/> </Sources> </Opinion> </Opinions></TrustEvaluationModel>

Permutation of opinions

Permutation of opinions

Permutation of Sources

Permutation of Sources

Trust Evaluation Model – Opinions

Trust Decision Model – Trust Values <Fuzzifier Name="trust_value">

<Terms> <Term Name="poor">

<Points><Point X="0.0" Y="1.0" /><Point X="0.5" Y="0.0" />

</Points> </Term> <Term Name="good">

<Points><Point X="0.0" Y="0.0" /><Point X="0.5" Y="1.0" /><Point X="1.0" Y="0.0" />

</Points> </Term> <Term Name="excellent">

<Points><Point X="0.5" Y="0.0" /><Point X="1.0" Y="1.0" />

</Points> </Term>

</Terms></Fuzzifier>

Term namesTerm names

The value of the trust_value variable has to be converted into degrees of membership for themembership functions defined on the variable.

Input variableInput variable

Membership functions

Membership functions

<Defuzzifier Name="trust_level" AccumulationMethod="MAX" DefuzzificationMethod="COG" DefaultValue="0"><Terms>

<Term Name="none"><Points>

<Point X="0.0" Y="0.0" /><Point X="0.1" Y="1.0" /><Point X="0.2" Y="0.0" />

</Points> </Term> <Term Name="limited">

<Points><Point X="0.2" Y="0.0" /><Point X="0.5" Y="1.0" /><Point X="0.8" Y="0.0" />

</Points> </Term> <Term Name="full">

<Points><Point X="0.8" Y="0.0" /><Point X="0.9" Y="1.0" /><Point X="1.0" Y="0.0" />

</Points> </Term>

</Terms></Defuzzifier>

Membership functions

Membership functions

Output variableOutput variable

Trust Decision Model – Trust Levels

Trust Decision Model – Fuzzy Interference

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IF trust_value IS poor THEN trust_level IS noneIF trust_value IS good THEN trust_level IS limitedIF trust_value IS excellent THEN trust_level IS full

trust level: 0.32

Accumulation Method: MAX

Defuziffication Method: COG

Implication Method: MINtrust value: 0.11

<GREPTrust:Report> <Resources> <Resource Id=“1" Value="0.11" Level="0.32"> <Rules> <Rule Id="3" Degree="0.0"/> <Rule Id="2" Degree="0.22"/> <Rule Id="1" Degree="0.78"/> </Rules> </Resource> <Resource Id=“2" Value="0.41" Level="0.46"> <Rules> <Rule Id="3" Degree="0.0"/> <Rule Id="2" Degree="0.82"/> <Rule Id="1" Degree="0.18"/> </Rules> </Resource> </Resources></GREPTrust:Report>

TDM: Trust Level

TDM: Trust Level

TDM: Degree membershipTDM: Degree membership

Trust Decision Model – Output

Questions