1. RATEWeb: Reputation Assessment Framework for Trust Establishment among Web Services Zaki Malik, Athman Bouguettaya Hung-Yuan Chung Yen-Cheng Lu

2. Outline Introduction RATEWeb Model Reputation Assessment Techniques Experiments Conclusion

3. Introduction 1.Trust in service-oriented environment 2.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 B2B 4.Business might outsource some of the functionality to other business 5.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.

4. 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.

5. 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.

6. Model Entities 1.Web services 2.Service Providers: a) one provider can provide one or more services b) a service is provided by a single service provider c) outsource 3.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)

7. 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

10. 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) 1.The providers can use service’s reputation when composing their CBs.  CB can reduce the risk of its own reputation getting tarnished. 2.Consumers can select the best CB based on the different CB’s individual reputation.

11. 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.

12. Definition  Community c i := (Identifier i, Category i, Generic-operation i, Members i ) 1.Identifier i : contains name and features of c i 2.Category i : contains areas of interests 3.G-operations i : summarizes the major functions needed by community members 4.Member i : a list of members. Members will support one or several of c i ‘s generic operation

13. Model Interactions 1.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 rating 2.ratings are keep local with the raters

15. Reputation Assessment

16. Web Service Reputation

17. Reputation Evaluation Metrics Rater Credibility Majority Rating Past Rating History Personal Experience for Credibility Evaluation Personal Preference Personal Experience for Reputation Assessment Temporal Sensitivity

18. Reputation Evaluation Metrics Rater Credibility Majority Rating Past Rating History Personal Experience for Credibility Evaluation Personal Preference Personal Experience for Reputation Assessment Temporal Sensitivity

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

20. 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

21. Rater Credibility In short: deduce more credibility if your opinion is different

22. 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

23. Rater Credibility

24. 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.

25. Personalized Preferences

26. Temporal Sensitivity

27. First-hand knowledge Finally,

28. Reputation Assessment

29. Experimental Evaluations () Parameter settings

30. # High credibility >> # Low credibility

31. # High credibility = # Low credibility

32. # High credibility << # Low credibility

35. Transaction Success Rate

36. Reputation Error

37. 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

38. Publish-subscribe model

39. Community broadcast model

40. Credibility-based model

41. Cost Analysis Parameter settings:

42. Cost Analysis