B-Trust: Bayesian Trust Framework for Pervasive Computing

  • Daniele Quercia
  • Stephen Hailes
  • Licia Capra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3986)


Without trust, pervasive devices cannot collaborate effectively, and without collaboration, the pervasive computing vision cannot be made a reality. Distributed trust frameworks may support trust and thus foster collaboration in an hostile pervasive computing environment. Existing frameworks deal with foundational properties of computational trust. We here propose a distributed trust framework that satisfies a broader range of properties. Our framework: (i) evolves trust based on a Bayesian formalization, whose trust metric is expressive, yet tractable; (ii) is lightweight; (iii) protects user anonymity, whilst being resistant to “Sybil attacks” (and enhancing detection of two collusion attacks); (iv) integrates a risk-aware decision module. We evaluate the framework through four experiments.


Direct Experience Decision Module Pervasive Computing Trust Level Trust Formation 
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 2006

Authors and Affiliations

  • Daniele Quercia
    • 1
  • Stephen Hailes
    • 1
  • Licia Capra
    • 1
  1. 1.Department of Computer ScienceUniversity College LondonLondonUK

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