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

Articulating Subjective Trust-Based Decision Strategies Utilizing the Reputation-Policy Trust Management Service. Y. Zetuny, G. Terstyanszky, S. Winter, P. Kacsuk Centre for Parallel Computing Cavendish School of Informatics University of Westminster. Trust Management. OVERVIEW

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

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  1. Articulating Subjective Trust-Based Decision Strategies Utilizing the Reputation-Policy Trust Management Service Y. Zetuny, G. Terstyanszky, S. Winter, P. Kacsuk Centre for Parallel ComputingCavendish School of Informatics University of Westminster University of Westminster – www.cpc.wmin.ac.uk

  2. Trust Management 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

  3. 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.

  4. 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

  5. Reputation-Based Trust Management 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).

  6. Trust Decision Strategy • 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 opinionsrepresenting 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.

  7. Trust Decision Strategy TDS = {TEM; (TDR1;TDR2; … ;TDRn)}

  8. Opinion Matrice 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. M(O) - opinion matrice (application or job-level) computed trust (fuzzy) value values are based on time series distribution, trust decay function, cut off time and weighted mean MS - opinion matrice set

  9. 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:

  10. Architecture of Reputation-Based Trust Management • 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

  11. Steps 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.

  12. Processing a Trust Query STEP1: Process TDS Evaluation Model STEP2: Process TDS Decision Model STEP3: Generate Reputation-Policy Report

  13. TDS – Fuzzy Interference Engine

  14. Trust Evaluation Model – Opinions <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 Sources

  15. 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> Input variable Term names Membership functions The value of the trust_value variable has to be converted into degrees of membership for the membership functions defined on the variable.

  16. Trust Decision Model – Trust Levels <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> Output variable Membership functions

  17. Trust Decision Model – Fuzzy Interference Implication Method: MIN trust value: 0.11 Accumulation Method: MAX IF trust_value IS poor THEN trust_level IS none IF trust_value IS good THEN trust_level IS limited IF trust_value IS excellent THEN trust_level IS full trust level: 0.32 Defuziffication Method: COG 17

  18. Trust Decision Model – Output <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: Degree membership

  19. Questions

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