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Abstract

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.

Keywords

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.

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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

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