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dc.contributor.authorTøndel, Inger Anne
dc.contributor.authorNyre, Åsmund Ahlmann
dc.contributor.authorBernsmed, Karin
dc.date.accessioned2017-02-09T14:25:28Z
dc.date.available2017-02-09T14:25:28Z
dc.date.created2012-03-08T10:17:37Z
dc.date.issued2011
dc.identifier.citationProceedings of the Sixth International Conference on Availability, Reliability and Securitynb_NO
dc.identifier.isbn978-1-4577-0979-1
dc.identifier.urihttp://hdl.handle.net/11250/2430175
dc.description.abstractThis 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.
dc.language.isoengnb_NO
dc.relation.ispartofProceedings of the Sixth International Conference on Availability, Reliability and Security
dc.titleLearning Privacy Preferencesnb_NO
dc.typeChapternb_NO
dc.source.pagenumber621-626nb_NO
dc.identifier.cristin914083
cristin.unitcode7401,90,13,0
cristin.unitnameSystemutvikling og sikkerhet
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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