RATEWeb: Reputation Assessment Framework for Trust Establishment among Web Services

Zaki Malik, Athman Bouguettaya

Hung-Yuan ChungYen-Cheng Lu

Outline

•Introduction•RATEWeb Model•Reputation Assessment Techniques•Experiments•Conclusion

Introduction

1.Trust in service-oriented environment2.The web has started a steady evolution to become a

“vibrant” environment where applications can be automatically invoked by other web clients.

3.B2C and B2B4.Business might outsource some of the functionality to

other business5.We expect enterprises are no longer a monolithic

organization, but a coupling of smaller Web-applications

6.Web services need to determine which other services can provide the required functionality, before they interact with them.

Introduction (cont.)

7. There are many web services having the same functionality. They need to compete with each other. A mechanism for the quality access service.

8.Web services are autonomous, priori unknown, and highly volatile (low reliability)

9.Reliable reputation systems increase user’s trust on the Web. eBay’s feedback Forum, deterring dishonest

behavior, and stimulating eBay’s growth.

RATEWeb (Reputation Assessment for Trust Establishment among Web Services)

1.It provides a comprehensive solution for assessing the reputation of service providers in a reliable, decentralized manner.

2.Different ratings are aggregated to derive a service provider’s reputation.

3.It takes into account the presence of malicious raters that may exhibit oscillating honest and dishonest behaviors.

Model Entities 1.Web services2.Service Providers: a) one provider can provide one or more services b) a service is provided by a single service provider c) outsource3.Services registries: a collection of descriptions of

Web services 4.Service consumers (a.k.a. client): invokes 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)

Scenario: Car Brokerage Application

1. A company deploys a car broker Web service (CB)2. CB is registered with service registries (Then

consumer can obtain details through the registry)3. CB may outsource from other web services.

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

4. Service providers may also act as consumers.5. A consumer access a CB service to buy a car. Then a

series of invocations would need to take place.6. The selection of a service by CB at each invocation

step can be done in two ways: with or without reputation system

Comparison No guarantees about the delivery of the required

functionality could be made before the actual interaction.

Scenario 1: (one monopoly)1. From the consumer’ respect, the scenario described is

far from optimal.

Scenario 2: (competition among CBs)2. The providers can use service’s reputation when

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

3. Consumers can select the best CB based on the different CB’s individual reputation.

Extension: Community

1.Community: a container that clumps together Web services related to a specific area of interest

2.All Web service that belongs to a given community share the same area of interest.

3.Responsibilities:a) Set reputation threshold.b) Set rules when a member’s reputation goes

below the threshold.c) Define reputation requirements for new

members.

Definition

Community ci := (Identifieri , Categoryi , Generic-operationi , Membersi )

1. Identifieri : contains name and features of ci

2. Categoryi: contains areas of interests

3. G-operationsi: summarizes the major functions needed by community members

4. Memberi: a list of members. Members will support one or several of ci‘s generic operation

Model Interactions1. Service providers can register their web services with

communities.2. The consumer can access service registries to get the

details of a communities and providers.3. Communities search their directories for the list of

providers that have registered their operations.4. Communities also contain a list of consumers that had

interacted with each members in the past. 5. The consumer then selects the best provider form the

list. The community only act as a directory of raters

1. not as a centralized repository of rating2. ratings are keep local with the raters

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

Web Service Reputation

• : The set of service consumers• : Personal evaluation, represents only

consumer ’s perception of the provider ’s reputation

• : Aggregation function

Reputation Evaluation Metrics

•Rater Credibility•Majority Rating•Past Rating History•Personal Experience for Credibility

Evaluation•Personal Preference•Personal Experience for Reputation

Assessment•Temporal Sensitivity

Reputation Evaluation Metrics

•Rater Credibility•Majority Rating•Past Rating History•Personal Experience for Credibility

Evaluation•Personal Preference•Personal Experience for Reputation

Assessment•Temporal Sensitivity

Rater Credibility

•In 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?

Rater Credibility

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

•Majority opinion:▫By K-means clustering

Rater Credibility• The 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 different

Rater Credibility• Idea 2: difference with the opinions in a time

period

Note that k has different meanings in the 2 eqs.

k: valid time lag

t: current timestamp

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 1.

2.

3.

4.

: “pessimism factor” – low-> optimistichigh->pessimistic

: “pessimism factor” – low-> pessimistichigh-> optimistic

Rater Credibility• Usefulness 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.

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

Temporal Sensitivity

•Reputation fader – fade out the out-dated ratings

•E.g., where is the total number of past transactions over which the reputation is to be evaluated

First-hand knowledge

•Finally,

Reputation Assessment

Experimental Evaluations ()

•Parameter settings

# High credibility >> # Low credibility

# High credibility = # Low credibility

# High credibility << # Low credibility

Low (Optimistic consumer)

High (Pessimistic consumer)

Transaction Success Rate

Reputation Error

Cost Analysis Experiments

•Runtime overhead mainly involves ▫Retrieving required information ▫Assimilate all the gathered information

•The cost is directly influenced by the reputation collection model used.▫Publish-subscribe model▫Community broadcast model▫Credibility-based model

Publish-subscribe model

Community broadcast model

Credibility-based model

Cost Analysis

•Parameter settings:

Cost Analysis