1. Reputation Management on the Service Web
- PhD Preliminary Proposal Defense -
Abdelmounaam Rezgui
Department of Computer Science
Virginia Tech

2. Outline Introduction Research Statement Research Plan and Methods
Preliminary Contribution
Current Publications
Related Work
Schedule for Completion

3. Introduction

4. Introduction A Web Service: Publish – Find - Invoke A software system
identified by a URI,
has public interfaces and bindings described using XML,
can be discovered by other software systems,
interacts using XML based messages.

5. Current Model for the Service Web

6. Trust on the Service Web
Interactions
Involve
Quality expectations
Exchange of sensitive information
Risks
Require
Trust

7. Trust on the Service Web
Trust: the belief that a service consumer has about the intention and ability of a service provider to act as expected

8. Significance of Trust on the Service Web
Horizontal Growth: Number of users
Vertical Growth: Number and types of applications
Value Growth: Worth of transactions

9. Trust on the Service Web
Challenges

10. Alternatives for Trust Establishment

11. Proposed Approach Reputation Management Definition: Approach
A service’s reputation within a community of consumers is a perception shared by some or all of the members of that community about that service
Approach
Consumers rate Web services
Consumers collaborate to assess services’ reputation
Trust in services derives from their reputation
Reputation-based service selection and composition

12. Outline Introduction Research Statement Research Plan and Methods
Preliminary Contribution
Current Publications
Related Work
Schedule for Completion

13. Research Plan Automatic Rating of Web Services
Models and Techniques for Reputation Management
Models for Rating Collection
Reputation Assessment
History-aware Reputation Assessment
Prevention and Detection of Reputation Tampering
Trust-based Selection and Composition

14. An Approach for Automatic Service Rating
Rating Scheme: set of rating rules used to automatically rate services
Generic
E.g., The service exchanges username/password with its consumers only through encrypted communication
Domain-specific
E.g., The pickup location is within 5 miles from the customer’s address
Specification and Interpretation of Rating Rules

15. Models and Metrics for Reputation Management
Centralized, Clique, Arbitrary P2P, Credibility-based P2P
System Metrics
Cohesion degree
Penetrability
Sensitivity
Scarcity
Consumer Metrics
Confidence
Credibility

16. Techniques for Reputation Management
Reputation Assessment in
Regular Environments
Scarce Environments
Discordant Environments
History-aware Reputation Assessment
Prevention and Detection of Reputation Tampering

17. Techniques for Reputation Management
Regular Reputation Environments: dense and relatively concordant
developing distributed algorithms that efficiently collect ratings from consumers and generate accurate reputation values for services
techniques and heuristics to optimize performance and accuracy

18. Techniques for Reputation Management
Scarce Reputation Environments: insufficient density
Statistical techniques
Accurate and fair initialization
assessing the reputation of newly deployed services (i.e., newcomers) for which only a few ratings are available.

19. Techniques for Reputation Management
Discordant Reputation Environments: large numbers of discordant ratings are available for aggregation
Rating Cleansing
Incremental Pruning
Successive iterations produce a relatively concordant environment
Outlier detection techniques
Clustering-merging
Extracts sets of cohesive clusters
Merge clusters to produce concordant environments

20. Techniques for Reputation Management
History-aware Reputation Assessment
Techniques that consider the “effect of history”
Comparison with amnesic techniques
Reputation Prediction

21. Techniques for Reputation Management
Prevention and Detection of Reputation Tampering
Collusion:
occurs when several raters collude with the malicious intention to improve or lower the reputation of one or more services
Whitewashing:
occurs when a service provider suspends and redeploys a service with a different identity with the malicious intention of clearing its reputation history by acquiring a fresh reputation

22. Service Selection & Composition
Trust Derivation
Formally capture the process of trust derivation
Study this process using various distribution for:
Consumer’s confidence in services’ reputation
Consumer’s past experience
Effect of past experience on consumers’ judgment
Reputation-based Selection
Clients are autonomous
May use different criteria for selection
Reputation-based Composition
Static Composition
Dynamic Composition

23. Implementation and Experimental Validation
Prototype Reputation System for the Service Web
Implementation
Testing
Experiments
Analysis

24. Outline Introduction Research Statement Research Plan and Methods
Preliminary Contribution
Current Publications
Related Work
Schedule for Completion

25. Preliminary Contribution
Preserving privacy in Web services
Application: Digital Government
Privacy-based Ranking of Web Services
A preliminary reputation system
Application: Digital Libraries

26. Preserving Privacy in Government Web Services
Objective:
Privacy preserving interoperation amongst Government Web services

27. Preserving Privacy in Government Web Services
Digital Privacy Credentials
E.g., user’s id, expiration date, set of valid operations that may be invoked, etc.
Data Filters
Credential Checking Module (CCM)
uses the credential received with the query to determine whether the sending entity is authorized to access the requested information
Query Rewriting Module (QRM)
rewrites the query so that the local privacy policy of the agency and overall privacy policy of the DG infrastructure are not violated

28. Privacy-based Ranking of Web Services
Objective: Enable users and agents to determine, a priori, privacy risks when interacting with Web services
Principle:

29. A Privacy Preserving Semantic Web Infrastructure for Digital Libraries
machines become much better able to process and understand the data that they merely display at present
explore the Web more aggressively than humans

30. A Privacy Preserving Semantic Web Infrastructure for Digital Libraries

31. Current Publications Book Chapters Journals
1. A. Rezgui, A. Bouguettaya, and Z. Malik. Information Security Policies and Actions in Modern Integrated Systems, chapter Using Reputation to Prerserve Privacy in the Semantic Web. Idea Group Publishing, March 2004.
2. A. Bouguettaya, A. Rezgui, B. Medjahed, and M. Ouzzani. Practical Handbook of Internet Computing, chapter Internet Computing Support for Digital Government. Chapman Hall & CRC Press, Baton Rouge, 2004.
Journals
3. A. Rezgui, A. Bouguettaya, and M. Eltoweissy. SemWebDL: A Privacy Preserving Semantic Web Infrastructure for Digital Libraries. Intl. Journal on Digital Libraries, 4(3): , 2004.
4. A. Rezgui, A. Bouguettaya, and M. Eltoweissy. Preserving Privacy in the Web: Facts, Challenges and Solutions. IEEE Security & Privacy, 1(6), November/December 2003.
5. B. Medjahed, A. Rezgui, A. Bouguettaya, and M. Ouzzani. Infrastructure for E-Government Web Services. IEEE Internet Computing, 7(1), January/February 2003.

32. Current Publications Conferences
6. A. Rezgui and A. Bouguettaya. Privacy-based Ranking of Web Services. In The 2nd International Conference on Service Oriented Computing (ICSOC), New York, USA, November 2004.
7. A. Bouguettaya, B. Medjahed, A. Rezgui, M. Ouzzani, X. Liu, and Q. Yu. WebDG-A Platform for E-Government Web Services. In The 1st International Workshop on Digital Government: Systems and Technologies (DGOV), Shanghai, China , November 2004.
8. A. Rezgui, A. Bouguettaya, and Z. Malik. A Reputation-based Approach to Preserving Privacy in Web Services. In The 4th VLDB Workshop on Technologies for E-Services (TES'03), Berlin, Germany, September 2003.
9. A. Bouguettaya, B. Medjahed, A. Rezgui, M. Ouzzani, and Z.Wen. Privacy preserving composition of government web services. In The 3rd NSF Conference for Digital Government Research (dg.o), Los Angeles, USA, May
10. A. Rezgui, Z. Wen, , and A. Bouguettaya. Preserving Privacy in Interoperable E-Government Databases. In The 3rd NSF Conference for Digital Government Research (dg.o 2002), pages 56-62, Los Angeles, USA, May
11. M. S. Marzouk, W. G. Aref, A. K. Elmagarmid, J. Fan, J. Guo, M. Hammad, I. F. Ilyas, S. Prabhakar, A. Rezgui, S. Teoh, E. Terzi, Y. Tu, and A. Vakali. A Distributed Database Server for Continuous Media (demo paper). In Proc. of the 18th IEEE International Conference on Data Engineering (ICDE), San Jose, California, USA, February 26-March
12. A. Rezgui, M. Ouzzani, A. Bouguettaya, and B. Medjahed. Preserving Privacy in Web Services. In Proc. of the 4th ACM Workshop on Information and Data Management (WIDM'02), pages 56{62, McLean, VA, November 2002.

33. Related Work Web services Game theory P2P networks
Information retrieval and search engines
Social networks
Grid computing systems
Multi-agents systems
Spam filtering

34. Related Work Web Services A few contributions
An ontology for Web service ratings and reputations (Maximilien and Singh, )
ratings of services (aggregated into reputations) are organized into an ontology and shared so as to facilitate service selection.
This model is expressed in DAML and includes domain independent as well as domain-specific attributes.
Services are rated manually
Trustworthy service composition (Singh, 2002)
Requirements and challenges

35. Related Work
Reputation model for ranking services (Emekci, Sahin, Agrawal, 2004)
Structured P2P framework for WS discovery based on:
Service functionality
Process behavior
Services vote for each other
Service selection in semantic grids (Majithia, Ali, and Rana, 2004)
Adaptive reputation-aware service discovery algorithm
A QoS metric for selecting Web service and providers (Kalepu, Krishnaswamy, Loke, 2004)
Reputation = f (rating, compliance, verity)

36. Related Work Game Theory
modeling different types of games and studying the effects of reputation in the considered models
E.g., When is reputation bad (Ely et al., 2005)
applying game theory to study the effect of reputation on economic, social, political, and psychological phenomena
E.g., Entrance deterrence problem
The chain store paradox (Selten, 1978)

37. Related Work P2P Networks
Identify reputable nodes so that non-reputable nodes may be prevented from affecting the system.
A peer can then use this information in decision making, e.g., who to download a file from
Examples:
EigenTrust (Kamvar, Schlosser, Garcia-Molina, 2003)
PeerTrust (Xiong,Liu, 2004)
Anonymous publishing (Dingledine et al., 2003)

38. Schedule for Completion