Electronic Commerce Research

, Volume 10, Issue 3–4, pp 331–362 | Cite as

A mechanism that provides incentives for truthful feedback in peer-to-peer systems

Article

Abstract

We propose a mechanism for providing the incentives for reporting truthful feedback in a peer-to-peer system for exchanging services (or content). This mechanism is to complement reputation mechanisms that employ ratings’ feedback on the various transactions in order to provide incentives to peers for offering better services to others. Under our approach, each of the transacting peers (rather than just the client) submits a rating on the performance of their mutual transaction. If these are in disagreement, then both transacting peers are punished, since such an occasion is a sign that one of them is lying. The severity of each peer’s punishment is determined by his corresponding non-credibility metric; this is maintained by the mechanism and evolves according to the peer’s record. When under punishment, a peer does not transact with others. We model the punishment effect of the mechanism in a peer-to-peer system as a Markov chain that is experimentally proved to be very accurate. According to this model, the credibility mechanism leads the peer-to-peer system to a desirable steady state isolating liars. Then, we define a procedure for the optimization of the punishment parameters of the mechanism for peer-to-peer systems of various characteristics. We experimentally prove that this optimization procedure is effective and necessary for the successful employment of the mechanism in real peer-to-peer systems. Then, the optimized credibility mechanism is combined with reputation-based policies to provide a complete solution for high performance and truthful rating in peer-to-peer systems. The combined mechanism was experimentally proved to deal very effectively with large fractions of collaborated liar peers that follow static or dynamic rational lying strategies in peer-to-peer systems with dynamically renewed population, while the efficiency loss induced to sincere peers by the presence of liars is diminished. Finally, we describe the potential implementation of the mechanism in real peer-to-peer systems.

Keywords

Credibility Rational adversaries Collusion Sybil attack Strategyproof Markov model Steady state 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dellarocas, C. (2003). Efficiency through feedback-contingent fees and rewards in auction marketplaces with adverse selection and moral hazard. In Proc. of the 3rd ACM conference on electronic commerce, San Diego, CA, USA, June 2003. Google Scholar
  2. 2.
    Papaioannou, T. G., & Stamoulis, G. D. (2006). Reputation-based policies that provide the right incentives in peer-to-peer environments. Computer Networks, 50(4), 563–578. (Special Issue on Management in Peer-to-Peer Systems: Trust, Reputation and Security). CrossRefGoogle Scholar
  3. 3.
    Yu, B., & Singh, M. P. (2002). Distributed reputation management for electronic commerce. Computational Intelligence, 18(4), 535–549. CrossRefGoogle Scholar
  4. 4.
    Aberer, K., & Despotovic, Z. (2001). Managing trust in a peer-to-peer information system. In Proc. of the 10th international conference on information and knowledge management, New York, NY, USA, November 2001. Google Scholar
  5. 5.
    Kamvar, S. D., Schlosser, M. T., & Garcia-Molina, H. (2003). EigenRep: reputation management in peer-to-peer networks. In: Proc. of the twelfth international world wide web conference, Budapest, Hungary, May 2003. Google Scholar
  6. 6.
    Papaioannou, T. G., & Stamoulis, G. D. (2005). An incentives’ mechanism promoting truthful feedback in peer-to-peer systems. In Proc. of the 5th IEEE/ACM international symposium in cluster computing and the grid, Cardiff, UK, May 2005. Google Scholar
  7. 7.
    Papaioannou, T. G., & Stamoulis, G. D. (2005). Optimizing an incentives’ mechanism for truthful feedback in virtual communities. In Proc. of the 4th international conference on autonomous agents and multiagent systems, Utrecht, The Netherlands, July 2005. Google Scholar
  8. 8.
    Dellarocas, C. (2000). Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In Proc. of the 2nd ACM conference on electronic commerce, Minneapolis, MN, USA, October 2000. Google Scholar
  9. 9.
    Ding, S., Zhao, S., Yuan, Q., Zhang, X., Fu, R., & Bergman, L. (2008). Boosting collaborative filtering based on statistical prediction errors. In RecSys ’08: Proceedings of the 2008 ACM conference on recommender systems (pp. 3–10). New York: ACM. CrossRefGoogle Scholar
  10. 10.
    Chen, M., & Singh, J. P. (2001). Computing and using reputations for internet ratings. In Proc. of the 3rd ACM conference on electronic commerce, New York, NY, USA, October 2001. Google Scholar
  11. 11.
    Schillo, M., Funk, P., & Rovatsos, M. (2000). Using trust for detecting deceitful agents in artificial societies. Applied Artificial Intelligence, 14(8), 825–848. CrossRefGoogle Scholar
  12. 12.
    Damiani, E., De Capitani di Vimercati, S., Paraboschi, S., & Samarati, P. (2003). Managing and sharing servents’ reputations in P2P systems. IEEE Transactions on Knowledge and Data Engineering, 15(4), 840–854. CrossRefGoogle Scholar
  13. 13.
    Xiong, L., & Liu, L. (2004). Peertrust: supporting reputation-based trust for peer-to-peer electronic communities. IEEE Transactions on Knowledge and Data Engineering, 16(7), 843–857. CrossRefGoogle Scholar
  14. 14.
    Uddin, M. G., Zulkernine, M., & Ahamed, S. I. (2008). Cat: a context-aware trust model for open and dynamic systems. In SAC ’08: Proceedings of the 2008 ACM symposium on applied computing (pp. 2024–2029). New York: ACM. CrossRefGoogle Scholar
  15. 15.
    Malaga, R. A. (2001). Web-based reputation management systems: Problems and suggested solutions. Electronic Commerce Research, 1(4), 403–417. CrossRefGoogle Scholar
  16. 16.
    Zhou, R., Hwang, K., & Cai, M. (2008). Gossiptrust for fast reputation aggregation in peer-to-peer networks. IEEE Transactions on Knowledge and Data Engineering, 20(9), 1282–1295. CrossRefGoogle Scholar
  17. 17.
    Feldman, M., Papadimitriou, C., Chuang, J., & Stoica, I. (2004). Free-riding and white-washing in peer-to-peer systems. In Proc. of the ACM SIGCOMM workshop on practice and theory of incentives in networked systems, Portland, Oregon, USA, September 2004. Google Scholar
  18. 18.
    Ngan, T.-W. J., Wallach, D. S., & Druschel, P. (2003). Enforcing fair sharing of peer-to-peer resources. In Proc. of the 2nd international workshop on peer-to-peer systems, Berkeley, CA, USA, February 2003. Google Scholar
  19. 19.
    Miller, N., Resnick, P., & Zeckhauser, R. (2002). Eliciting honest feedback: the peer prediction method. Management Science, 51(9), 1359–1373. CrossRefGoogle Scholar
  20. 20.
    Jurca, R., & Faltings, B. (2004). An incentive compatible reputation mechanism. In Proc. of IEEE conference on electronic commerce, Newport Beach, CA, USA, June 2004. Google Scholar
  21. 21.
    Goel, S., Reeves, D. M., & Pennock, D. M. (2009). Collective revelation: a mechanism for self-verified, weighted, and truthful predictions. In EC ’09: Proceedings of the tenth ACM conference on electronic commerce (pp. 265–274). New York: ACM. CrossRefGoogle Scholar
  22. 22.
    Jurca, R., & Faltings, B. (2004). Eliciting truthful feedback for binary reputation mechanisms. In Proc. of IEEE/WIC/ACM international conference on web intelligence, Beijing, China, September 2004. Google Scholar
  23. 23.
    Dewan, P., & Dasgupta, P. (2009). P2p reputation management using distributed identities and decentralized recommendation chains. IEEE Transactions on Knowledge and Data Engineering, 99(1). Google Scholar
  24. 24.
    Resnick, P., & Sami, R. (2009). Sybilproof transitive trust protocols. In EC ’09: Proceedings of the tenth ACM conference on electronic commerce (pp. 345–354). New York: ACM. CrossRefGoogle Scholar
  25. 25.
    Dellarocas, C. (2005). Reputation mechanism design in online trading environments with pure moral hazard. Information Systems Research, 16(2), 209–230. CrossRefGoogle Scholar
  26. 26.
    Jøsang, A., Hird, S., & Faccer, E. (2003). Simulating the effect of reputation systems on e-markets. In Proc. of the 1st international conference on trust management, Crete, Greece, May 2003. Google Scholar
  27. 27.
    Antoniadis, P., Courcoubetis, C., Mason, R., Papaioannou, T. G., Stamoulis, G. D., & Weber, R. Results of peer-to-peer market models, September 2004. Project IST MMAPPS: Deliverable 8. Available at: http://www.mmapps.info.
  28. 28.
    Pretty good privacy. http://www.pgp.com/.

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Thanasis G. Papaioannou
    • 1
  • George D. Stamoulis
    • 1
  1. 1.Department of Computer ScienceAthens University of Economics and BusinessAthensGreece

Personalised recommendations