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

TDS – Fuzzy Interference Engine

Permutation of opinions Permutation of Sources Trust Evaluation Model – Opinions

Trust Decision Model – Trust Values Term names The value of the trust_value variable has to be converted into degrees of membership for the membership functions defined on the variable. Input variable Membership functions

Membership functions Output variable Trust Decision Model – Trust Levels

Trust Decision Model – Fuzzy Interference 17 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 Accumulation Method: MAX Defuziffication Method: COG Implication Method: MIN trust value: 0.11

TDM: Trust Level TDM: Degree membership Trust Decision Model – Output

Questions