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Privacy in Recommender Systems

  • Arjan J. P. JeckmansEmail author
  • Michael Beye
  • Zekeriya Erkin
  • Pieter Hartel
  • Reginald L. Lagendijk
  • Qiang Tang
Chapter
Part of the Computer Communications and Networks book series (CCN)

Abstract

In many online applications, the range of content that is offered to users is so wide that a need for automated recommender systems arises. Such systems can provide a personalized selection of relevant items to users. In practice, this can help people find entertaining movies, boost sales through targeted advertisements, or help social network users meet new friends.indent To generate accurate personalized recommendations, recommender systems rely on detailed personal data on the preferences of users. Examples are ratings, consumption histories, and personal profiles. Recommender systems are useful; however, the privacy risks associated to gathering and processing personal data are often underestimated or ignored. Many users are not sufficiently aware if and how much of their data is collected, if such data is sold to third parties, or how securely it is stored and for how long. indent This chapter aims to provide insight into privacy in recommender systems. First, we discuss different types of existing recommender systems. Second, we give an overview of the data that is used in recommender systems. Third, we examine the associated risks to data privacy. Fourth, relevant research areas for privacy-protection techniques and their applicability to recommender systems are discussed. Finally, we conclude with a discussion on applying and combining different privacy-protection techniques in real-world settings, making clear mappings to reflect typical relations between recommender system types, information types, particular privacy risks, and privacy-protection techniques.

Keywords

Service Provider Recommender System Privacy Concern Homomorphic Encryption Differential Privacy 
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.

Notes

Acknowledgements

The research for this work was carried out within the Kindred Spirits project, part of the STW Sentinels research program.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Arjan J. P. Jeckmans
    • 1
    Email author
  • Michael Beye
    • 2
  • Zekeriya Erkin
    • 2
  • Pieter Hartel
    • 1
  • Reginald L. Lagendijk
    • 2
  • Qiang Tang
    • 1
  1. 1.Distributed and Embedded Security, Faculty of EEMCSUniversity of TwenteEnschedeThe Netherlands
  2. 2.Information Security and Privacy Lab, Faculty of EEMCSDelft University of TechnologyDelftThe Netherlands

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