RATEWeb - PowerPoint Presentation

Slide1

RATEWeb: Reputation Assessment Framework for Trust Establishment among Web ServicesZaki Malik, Athman Bouguettaya

Hung-Yuan

Chung

Yen-Cheng LuSlide2

OutlineIntroductionRATEWeb ModelReputation Assessment TechniquesExperimentsConclusionSlide3

IntroductionTrust in service-oriented environmentThe web has started a steady evolution to become a “vibrant” environment where applications can be

automatically invoked

by other web clients.

B2C

and

B2B

Business might outsource some of the functionality to other business

We expect enterprises are

no longer a monolithic

organization,

but a coupling of smaller Web-applications

Web services need to determine

which other services can provide the required functionality, before they interact with them. Slide4

Introduction (cont.)There are many web services having the same functionality. They need to compete with

each other.

A mechanism

for the quality access service.

Web services are autonomous, priori unknown, and highly volatile (low

reliability)

Reliable reputation systems increase user’s trust on the Web.

eBay’s feedback Forum, deterring dishonest behavior, and stimulating eBay’s growth.Slide5

RATEWeb (Reputation Assessment for Trust Establishment among Web Services)It provides a comprehensive solution for assessing the reputation of service providers in a reliable, decentralized manner.Different ratings are aggregated to derive a service provider’s reputation.

It takes into account the presence of

malicious raters

that may exhibit oscillating honest and dishonest behaviors.Slide6

Model Entities Web servicesService Providers: a) one provider can provide one or more services

b) a service is provided by a

single

service provider

c) outsource

Services registries

: a collection of descriptions of Web services

Service consumers

(a.k.a. client):

i

nvokes a Web service

A human user uses a Web service

proxy

. The human user only communicates his/her needs to the service proxy, and all decisions are all taken by the service proxy. (everything is automated)Slide7

Scenario: Car Brokerage ApplicationA company deploys a car broker Web service (CB)

CB is registered with service registries (Then consumer can obtain details through the registry)

CB may outsource from other web services.

e.g., car dealer, lemon check, financing, credit history, insurance

S

ervice providers may also act as consumers.

A consumer access a CB service to buy a car. Then a series of invocations would need to take place.

The selection of a service by CB at each invocation step can be done in two ways:

with or without

reputation systemSlide8
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ComparisonNo guarantees about the delivery of the required functionality could be made before the actual interaction.Scenario 1: (one monopoly)

From the consumer’ respect, the scenario described is far from optimal.

Scenario 2: (competition among CBs)

The providers can use service’s reputation when composing their CBs

.

CB can reduce the risk of its own reputation getting tarnished.

Consumers can select the

best CB

based on the

different

CB’s individual reputation.Slide11

Extension: Community Community: a container that clumps together Web services related to a specific area of interestAll Web service that belongs to a given community share the same area of interest.

Responsibilities:

Set reputation

threshold

.

Set

rules

when a member’s reputation goes below the threshold.

Define reputation requirements for new members. Slide12

DefinitionCommunity ci := (Identifier

i

,

Category

i

, Generic-

operation

i

,

Members

i

)

Identifier

i

: contains name and features of c

i

Category

i

: contains areas of interests

G-

operations

i

: summarizes the major functions needed by community members

Member

i

: a list of members. Members will support one or several of

c

i

‘s

generic operation Slide13

Model InteractionsService providers can register their web services with communities.The consumer can access service registries to get the details of a communities and providers.Communities search their directories for the list of providers that have registered their operations.

Communities also contain a list of consumers that had interacted with each members in the past.

The consumer then selects the best provider form the list.

The community only act as a directory of raters

not as a centralized repository of rating

ratings are

keep local

with the ratersSlide14
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Reputation Assessment Parameters reflecting the Quality of Web Services:

Provider-promised

Consumer-expected

Service-delivered

Quality parameter

is the

kth

quality parameter

When a service requester

invokes the service

, each quality parameter

in

gets assigned a

delivered quality value

 Slide16

Web Service Reputation : The set of service consumers

: Personal evaluation,

represents only consumer

’s perception

of

the

provider

’s reputation

: Aggregation function

 Slide17

Reputation Evaluation MetricsRater CredibilityMajority RatingPast Rating HistoryPersonal Experience for Credibility EvaluationPersonal PreferencePersonal Experience for Reputation AssessmentTemporal SensitivitySlide18

Reputation Evaluation MetricsRater CredibilityMajority RatingPast Rating HistoryPersonal Experience for Credibility EvaluationPersonal PreferencePersonal Experience for Reputation AssessmentTemporal SensitivitySlide19

Rater CredibilityIn order to cater for such bad-mouthing or collusion possibilities, the system should weigh highly credible raters than low credible raters

How to get the

s?

 Slide20

Rater CredibilityIdea 1: “if the reported rating agrees with the majority opinion, the rater’s credibility is increased, and decreased otherwise”Majority opinion:

By K-means clusteringSlide21

Rater CredibilityThe change in credibility due to majority rating, denoted by is defined as:

where

is the standard deviation in all the reported ratings and

is the reported rating (of each rater),

note that the k is different from the clustering

In short: deduce more credibility if your opinion is differentSlide22

Rater CredibilityIdea 2: difference with the opinions in a time periodNote that k has different meanings in the 2 eqs

.

k

: valid time lag

t: current timestampSlide23

Rater Credibility

Based on

, the authors suggest several ways to estimate the credibility

General form:

is the credibility adjustment normalizing factor

is credibility change due to

: “pessimism factor” –

low-> optimistic

high->pessimistic

: “pessimism factor” –

low-> pessimistic

high-> optimistic

 Slide24

Rater CredibilityUsefulness factor – “The usefulness of a service is required to calculate a service rater’s “propensity to default,” i.e., the service rater’s tendency to provide false/incorrect ratings.”

where

Ui

is the

submission where the rater was termed “

useful”

and

Vx

denotes the

total number of ratings

submissions by

that service

.Slide25

Personalized Preferences

:

the

rating assigned to attribute

by the

service rater

for service provider

in transaction

,

:

the

total number of attributes

:

the preference

of the service consumer for attribute

 Slide26

Temporal SensitivityReputation fader – fade out the out-dated ratingsE.g., where

is the total number of

past

transactions

over

which the

reputation is to be evaluated

 Slide27

First-hand knowledgeFinally,Slide28

Reputation AssessmentSlide29

Experimental Evaluations ()Parameter settingsSlide30

# High credibility >> # Low credibilitySlide31

# High credibility = # Low credibilitySlide32

# High credibility << # Low credibilitySlide33

Low (Optimistic consumer)

 Slide34

High (Pessimistic consumer) Slide35

Transaction Success RateSlide36

Reputation ErrorSlide37

Cost Analysis ExperimentsRuntime overhead mainly involves Retrieving required information Assimilate all the gathered informationThe cost is directly influenced by the reputation collection model used.Publish-subscribe modelCommunity broadcast modelCredibility-based modelSlide38

Publish-subscribe modelSlide39

Community broadcast modelSlide40

Credibility-based modelSlide41

Cost Analysis Parameter settings:Slide42

Cost AnalysisSlide43