Auditing and inference control for privacy preservation in uncertain environments

Authors:
Xiangdong An;Dawn Jutla;Nick Cercone
Affiliations:
Faculty of Computer Science, Dalhousie University, Halifax, Canada;Department of Finance, Information Systems, and Management Science, Saint Mary's University, Halifax, Canada;Faculty of Computer Science, Dalhousie University, Halifax, Canada
Venue:
EuroSSC'06 Proceedings of the First European conference on Smart Sensing and Context
Year:
2006

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Abstract

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.