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International Conference on User Modeling, Adaptation, and Personalization

UMAP 2013: User Modeling, Adaptation, and Personalization pp 310–317Cite as

A Framework for Privacy-Aware User Data Trading

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7899))

Abstract

Data about users is rapidly growing, collected by various online applications and databases. The ability to share user data across applications can offer benefits to user in terms of personalized services, but at the same time poses privacy risks of disclosure of personal information. Hence, there is a need to ensure protection of user privacy while enabling user data sharing for desired personalized services. We propose a policy framework for user data sharing based on the purpose of adaptation. The framework is based on the idea of a market, where applications can offer and negotiate user data sharing with other applications according to an explicit user-editable and negotiable privacy policy that defines the purpose, type of data, retention period and price.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Saskatchewan, 110 Science Place, S7N 5C9, Saskatoon, Canada

    Johnson Iyilade & Julita Vassileva

Authors
  1. Johnson Iyilade
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  2. Julita Vassileva
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Editor information

Editors and Affiliations

  1. Department of Computer and Information Science, University of Delaware, 19716, Delaware, DE, USA

    Sandra Carberry

  2. School of Computing, National College of Ireland, Mayor Street, IFSC, Dublin 1, Ireland

    Stephan Weibelzahl

  3. Dipartimento di Ingegneria, Roma Tre University, Via della Vasca Navale, 79, 00146, Rome, Italy

    Alessandro Micarelli

  4. Department of Computer Science, University of Bari Aldo Moro, 70126, Bari, Italy

    Giovanni Semeraro

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Iyilade, J., Vassileva, J. (2013). A Framework for Privacy-Aware User Data Trading. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_28

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  • DOI: https://doi.org/10.1007/978-3-642-38844-6_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38843-9

  • Online ISBN: 978-3-642-38844-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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