Privacy in Recommender Systems

Arjan Jeckmans*, Michael Beye, Zekeriya Erkin, Pieter H. Hartel, Reginald L. Lagendijk, Qiang Tang

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    3 Citations (Scopus)
    568 Downloads (Pure)

    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. 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. 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.
    Original languageEnglish
    Title of host publicationSocial Media Retrieval
    EditorsNaeem Ramzan, Roelof van Zwol, Jong-Seok Lee, Kai Clüver, Xian-Sheng Hua
    Place of PublicationLondon
    PublisherSpringer
    Pages263-281
    Number of pages19
    ISBN (Print)978-1-4471-4554-7
    DOIs
    Publication statusPublished - Jan 2013

    Publication series

    NameComputer Communications and Networks
    PublisherSpringer Verlag
    ISSN (Print)1617-7975

    Keywords

    • Privacy-protection techniques
    • SCS-Cybersecurity
    • Privacy
    • Recommender systems

    Fingerprint Dive into the research topics of 'Privacy in Recommender Systems'. Together they form a unique fingerprint.

  • Cite this

    Jeckmans, A., Beye, M., Erkin, Z., Hartel, P. H., Lagendijk, R. L., & Tang, Q. (2013). Privacy in Recommender Systems. In N. Ramzan, R. van Zwol, J-S. Lee, K. Clüver, & X-S. Hua (Eds.), Social Media Retrieval (pp. 263-281). (Computer Communications and Networks). London: Springer. https://doi.org/10.1007/978-1-4471-4555-4_12