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Learning Privacy Preferences

Tøndel, Inger Anne; Nyre, Åsmund Ahlmann; Bernsmed, Karin
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URI
http://hdl.handle.net/11250/2430175
Date
2011
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  • Publikasjoner fra CRIStin - SINTEF AS [4329]
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Original version
Proceedings of the Sixth International Conference on Availability, Reliability and Security  
Abstract
This paper suggests a machine learning approach to preference generation in the context of privacy agents. With this solution, users are relieved from the complex task of specifying their preferences beforehand, disconnected from actual situations. Instead, historical privacy decisions are used as a basis for providing privacy recommendations to users in new situations. The solution also takes into account the reasons why users act as they do, and allows users to benefit from information on the privacy trade-offs made by others.

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