Robustness of Trust and Reputation Systems: Does It Matter?

  • Audun Jøsang
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 374)


Trust and reputation systems provide a foundation for security, stability, and efficiency in the online environment because of their ability to stimulate quality and to sanction poor quality. Trust and reputation scores are assumed to represent and predict future quality and behaviour and thereby to provide valuable decision support for relying parties. This assumption depends on two factors, primarily that trust and reputation scores faithfully reflect past observed quality, and secondly that future quality will be truly similar to that represented by the scores. Unfortunately, poor robustness of trust and reputation systems often makes it relatively easy to manipulate these factors, so that the fundamental assumption behind trust and reputation systems becomes questionable. On this background we discuss to what degree robustness against strategic manipulation is important for the usefulness of trust and reputation systems in general.

This paper is the printed version of the inaugural William Winsborough Commemorative Address at the IFIP Trust Management Conference 2012 in Surat.


Service Provider Multiagent System Online Community Reputation System Reputation Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., Gonçalves, M.: Detecting spammers and content promoters in online video social networks. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 620–627. ACM, New York (2009)CrossRefGoogle Scholar
  2. 2.
    Clausen, A.: The Cost of Attack of PageRank. In: Proceedings of the International Conference on Agents, Web Technologies and Internet Commerce (IAWTIC 2004), Gold Coast (July 2004)Google Scholar
  3. 3.
    Dellarocas, C.: Immunizing Online Reputation Reporting Systems Against Unfair Ratings and Discriminatory Behavior. In: ACM Conference on Electronic Commerce, pp. 150–157 (2000)Google Scholar
  4. 4.
    Dini, F., Spagnolo, G.: Buiyng reputation on eBay: Do recent changes help? International Journal of Electronic Business 7(6), 581–598 (2009)CrossRefGoogle Scholar
  5. 5.
    Duan, H., Liu, F.: Building and Managing Reputation in the Environment of Chinese E-commerce: A Case Study on Taobao. In: Kerkar, R., Badica, C. (eds.) Proc. of the 2nd International Conference on Web Intelligence, Mining and Semantics (WIMS 2012), Craiova, Romania (June 2012)Google Scholar
  6. 6.
    Farmer, R., Glass, B.: Building Web Reputation Systems. O’Reilly Media / Yahoo Press (March 2010)Google Scholar
  7. 7.
    Gilbert, E., Karahalios, K.: Understanding deja reviewers. In: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, CSCW 2010, pp. 225–228. ACM, New York (2010)CrossRefGoogle Scholar
  8. 8.
    Golbeck, J.: Trust on the World Wide Web: A Survey. Foundations and Trends in Web Science 1(2) (2008)Google Scholar
  9. 9.
    Hoffman, K., Zage, D., Nita-Rotaru, C.: A Survey of Attack and Defense Techniques for Reputation Systems. ACM Computing Surveys 42(1) (December 2009) (to appear)Google Scholar
  10. 10.
    Jøsang, A.: Online Reputation Systems for the Health Sector. Electronic Journal of Health Informatics 3(1), e8 (2008)Google Scholar
  11. 11.
    Jøsang, A., Golbeck, J.: Challenges for Robust of Trust and Reputation Systems. In: Proceedings of the 5th International Workshop on Security and Trust Management (STM 2009), Saint Malo (September 2009)Google Scholar
  12. 12.
    Jøsang, A., Ismail, R., Boyd, C.: A Survey of Trust and Reputation Systems for Online Service Provision. Decision Support Systems 43(2), 618–644 (2007)CrossRefGoogle Scholar
  13. 13.
    Kerr, R.: Smart Cheaters Do Prosper: Defeating Trust and Reputation Systems. In: Proceedings of the 8th Int. Joint Conference on Autonomous Agents & Multiagent Systems, AAMAS (July 2009)Google Scholar
  14. 14.
    Kerr, R., Cohen, R.: An Experimental Testbed for Evaluation of Trust and Reputation Systems. In: Ferrari, E., Li, N., Bertino, E., Karabulut, Y. (eds.) IFIPTM 2009. IFIP AICT, vol. 300, pp. 252–266. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Lim, E.-P., Nguyen, V.-A., Jindal, N., Liu, B., Lauw, H.W.: Detecting Product Review Spammers Using Rating Behaviors. In: Huang, J., et al. (eds.) Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 939–948. ACM, New York (2010)Google Scholar
  16. 16.
    Marsh, S.: Formalising Trust as a Computational Concept. PhD thesis, University of Stirling (1994)Google Scholar
  17. 17.
    Masum, H., Newmark, C., Tovey, M.: The Reputation Society: How Online Opinions Are Reshaping the Offline World. The Information Society Series. MIT Press (2012)Google Scholar
  18. 18.
    Court of appeal of the state of California. Roger M. Grace vs. eBay. B168765, Los Angeles County, Super. Ct. No. BS288836 (2004)Google Scholar
  19. 19.
    Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, HLT 2011, vol. 1, pp. 309–319. Association for Computational Linguistics (2011)Google Scholar
  20. 20.
    Resnick, P., Zeckhauser, R.: Trust Among Strangers in Internet Transactions: Empirical Analysis of eBay’s Reputation System. In: Baye, M.R. (ed.) The Economics of the Internet and E-Commerce. Advances in Applied Microeconomics, vol. 11. Elsevier Science (2002)Google Scholar
  21. 21.
    Resnick, P., Zeckhauser, R., Friedman, R., Kuwabara, K.: Reputation Systems. Communications of the ACM 43(12), 45–48 (2000)CrossRefGoogle Scholar
  22. 22.
    Resnick, P., Zeckhauser, R., Swanson, J., Lockwood, K.: The Value of Reputation on eBay: A Controlled Experiment. Experimental Economics 9(2), 79–101 (2006), CrossRefGoogle Scholar
  23. 23.
    Withby, A., Jøsang, A., Indulska, J.: Filtering Out Unfair Ratings in Bayesian Reputation Systems. In: Proceedings of the 7th Int. Workshop on Trust in Agent Societies (at AAMAS 2004). ACM (2004)Google Scholar
  24. 24.
    Wu, G., Greene, D., Cunningham, P.: Merging multiple criteria to identify suspicious reviews. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 241–244. ACM, New York (2010)CrossRefGoogle Scholar
  25. 25.
    Wu, G., Greene, D., Smyth, B., Cunningham, P.: Distortion as a validation criterion in the identification of suspicious reviews. In: Proceedings of the First Workshop on Social Media Analytics, SOMA 2010, pp. 10–13. ACM, New York (2010)CrossRefGoogle Scholar
  26. 26.
    Yu, B., Singh, M.P.: Detecting Deception in Reputation Management. In: Proceedings of the Second Int. Joint Conference on Autonomous Agents & Multiagent Systems (AAMAS), pp. 73–80. ACM (2003)Google Scholar
  27. 27.
    Zhang, H., Goel, A., Govindan, R., Mason, K., Van Roy, B.: Making Eigenvector-Based Reputation Systems Robust to Collusion. In: Leonardi, S. (ed.) WAW 2004. LNCS, vol. 3243, pp. 92–104. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Audun Jøsang
    • 1
  1. 1.University of OsloNorway

Personalised recommendations