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Privacy in Recommender Systems

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Book cover Social Media Retrieval

Part of the book series: Computer Communications and Networks ((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.

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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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Authors and Affiliations

  1. Distributed and Embedded Security, Faculty of EEMCS, University of Twente, Enschede, The Netherlands

    Arjan J. P. Jeckmans, Pieter Hartel & Qiang Tang

  2. Information Security and Privacy Lab, Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands

    Michael Beye, Zekeriya Erkin & Reginald L. Lagendijk

Authors
  1. Arjan J. P. Jeckmans
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  2. Michael Beye
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  3. Zekeriya Erkin
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  4. Pieter Hartel
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  5. Reginald L. Lagendijk
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  6. Qiang Tang
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Corresponding author

Correspondence to Arjan J. P. Jeckmans .

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Editors and Affiliations

  1. Department of Electronic Engineering, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom

    Naeem Ramzan

  2. Director of Product Innovation, Search, Netflix, Winchester Circle 100, Los Gatos, 95032, California, USA

    Roelof van Zwol

  3. School of Integrated Technology, Yonsei University, 162-1 Songdo-dong, Yeonsu-gu, Incheon, 406-840, Korea, Republic of (South Korea)

    Jong-Seok Lee

  4. Institut für Telekommunikationssysteme, Technische Universität Berlin, Einsteinufer 17, Berlin, 10587, Germany

    Kai Clüver

  5. Media Computing Group, Microsoft Research, 555 108th Ave NE, Bellevue, 98004, Washington, USA

    Xian-Sheng Hua

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Jeckmans, A.J.P., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R.L., Tang, Q. (2013). Privacy in Recommender Systems. In: Ramzan, N., van Zwol, R., Lee, JS., Clüver, K., Hua, XS. (eds) Social Media Retrieval. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4555-4_12

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  • DOI: https://doi.org/10.1007/978-1-4471-4555-4_12

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