Auditing and Inference Control for Privacy Preservation in Uncertain Environments

  • Xiangdong An
  • Dawn Jutla
  • Nick Cercone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4272)


In ubiquitous environments, context-aware agents have been developed to obtain, understand and share local contexts with each other so that the environments could be integrated seamlessly. Context sharing among agents should be made privacy-conscious. Privacy preferences are generally specified to regulate the exchange of the contexts, where who have rights to have what contexts are designated. However, the released contexts could be used to derive those unreleased. To date, there have been very few inference control mechanisms specifically tailored to context management in ubiquitous environments, especially when the environments are uncertain. In this paper, we present a Bayesian network-based inference control method to prevent privacy-sensitive contexts from being derived from those released in ubiquitous environments. We use Bayesian networks because the contexts of a user are generally uncertain, especially from somebody else’s point of view.


Bayesian Network Uncertain Environment Privacy Preservation Conditional Probability Distribution Privacy Preference 
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

  • Xiangdong An
    • 1
    • 2
  • Dawn Jutla
    • 2
  • Nick Cercone
    • 1
  1. 1.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada
  2. 2.Department of Finance, Information Systems, and Management ScienceSaint Mary’s UniversityHalifaxCanada

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