In modern global networks, principals usually have incomplete information about each other. Therefore trust and reputation frameworks have been recently adopted to maximise the security level by basing decision making on estimated trust values for network peers. Existing models for trust and reputation have ignored dynamic behaviours, or introduced ad hoc solutions. In this paper, we introduce the HMM-based reputation model for network principals, where the dynamic behaviour of each one is represented by a hidden Markov model (HMM). We describe the elements of this novel reputation model. In particular we detail the representation of reputation reports. We also describe a mixing scheme that efficiently approximates the behaviour of a trustee given multiple reports about it from different sources.


Hide Markov Model Predictive Distribution Reputation System Observation Sequence Beta Model 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baum, L.E., Petrie, T.: Statistical inference for probabilistic functions of finite-state Markov chains. Annals of Mathematical Statistics 37(6), 1554–1563 (1966)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains. The Annals of Mathematical Statistics 41(1), 164–171 (1970)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Brémaud, P.: Markov chains: Gibbs fields, Monte Carlo simulation, and queues. Springer (1998)Google Scholar
  4. 4.
    Buchegger, S., Le Boudec, J.: A Robust Reputation System for Peer-to-Peer and Mobile Ad-hoc Networks. In: P2PEcon 2004 (2004)Google Scholar
  5. 5.
    Cahill, V., Gray, E., Seigneur, J.M., Jensen, C.D., Chen, Y., Shand, B., Dimmock, N., Twigg, A., Bacon, J., English, C., Wagealla, W., Terzis, S., Nixon, P., Serugendo, G., Bryce, C., Carbone, M., Krukow, K., Nielsen, M.: Using trust for secure collaboration in uncertain environments. IEEE Pervasive Computing 2(3), 52–61 (2003)CrossRefGoogle Scholar
  6. 6.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory, 2nd edn. Wiley Series in Telecommunications and Signal Processing. Wiley-Interscience (July 2006)Google Scholar
  7. 7.
    ElSalamouny, E., Krukow, K., Sassone, V.: An analysis of the exponential decay principle in probabilistic trust models. Theoretical Computer Science 410(41), 4067–4084 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    ElSalamouny, E., Sassone, V.: An hmm-based reputation model. Technical report, INRIA (2013),
  9. 9.
    ElSalamouny, E., Sassone, V., Nielsen, M.: HMM-based trust model. In: Degano, P., Guttman, J. (eds.) FAST 2009. LNCS, vol. 5983, pp. 21–35. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Grimmet, G., Stirzaker, D.: Probability and Random Processes, 3rd edn. Oxford University Press (2001)Google Scholar
  11. 11.
    Jøsang, A., Haller, J.: Dirichlet reputation systems. In: The Second International Conference on Availability, Reliability and Security, ARES 2007, pp. 112–119 (2007)Google Scholar
  12. 12.
    Jøsang, A., Ismail, R.: The beta reputation system. In: Proceedings from the 15th Bled Conference on Electronic Commerce, Bled (2002)Google Scholar
  13. 13.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Annals of Mathematical Statistics 22(1), 79–86 (1951)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Mui, L., Mohtashemi, M., Halberstadt, A.: A computational model of trust and reputation (for ebusinesses). In: Proceedings from 5th Annual Hawaii International Conference on System Sciences, HICSS 2002, p. 188. IEEE (2002)Google Scholar
  15. 15.
    Nielsen, M., Krukow, K., Sassone, V.: A bayesian model for event-based trust. Electronic Notes in Theoretical Computer Science (ENTCS) 172, 499–521 (2007)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Norris, J.R.: Markov chains. Cambridge University Press (1997)Google Scholar
  17. 17.
    Rabiner, L., Juang, B.H.: Fundamentals of Speech Recognition. United states ed edn. Prentice Hall PTR (April 1993)Google Scholar
  18. 18.
    Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  19. 19.
    Teacy, W., Patel, J., Jennings, N., Luck, M.: Travos: Trust and reputation in the context of inaccurate information sources. Autonomous Agents and Multi-Agent Systems 12, 183–198 (2006)CrossRefGoogle Scholar
  20. 20.
    Xiong, L., Liu, L.: PeerTrust: Supporting reputation-based trust for peer-to-peer electronic communities. IEEE Transactions on Knowledge and Data Engineering 16(7), 843–857 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ehab ElSalamouny
    • 1
    • 2
  • Vladimiro Sassone
    • 3
  1. 1.INRIAFrance
  2. 2.Faculty of Computer and Information ScienceSuez Canal UniversityEgypt
  3. 3.ECSUniversity of SouthamptonUK

Personalised recommendations