Privacy aspects of recommender systems

Arik Friedman, Bart P. Knijnenburg, Kris Vanhecke, Luc Martens, Shlomo Berkovsky

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

35 Citations (Scopus)

Abstract

The popularity of online recommender systems has soared; they are deployed in numerous websites and gather tremendous amounts of user data that are necessary for recommendation purposes. This data, however, may pose a severe threat to user privacy, if accessed by untrusted parties or used inappropriately. Hence, it is of paramount importance for recommender system designers and service providers to find a sweet spot, which allows them to generate accurate recommendations and guarantee the privacy of their users. In this chapter we overview the state of the art in privacy enhanced recommendations. We analyze the risks to user privacy imposed by recommender systems, survey the existing solutions, and discuss the privacy implications for the users of recommenders. We conclude that a considerable effort is still required to develop practical recommendation solutions that provide adequate privacy guarantees, while at the same time facilitating the delivery of high-quality recommendations to their users.
Original languageEnglish
Title of host publicationRecommender systems handbook
EditorsFrancesco Ricci, Lior Rokach, Bracha Shapira
PublisherSpringer, Springer Nature
Chapter19
Pages649-688
Number of pages40
EditionSecond
ISBN (Electronic)9781489976376
ISBN (Print)9781489976369
DOIs
Publication statusPublished - 2015
Externally publishedYes

Keywords

  • recommender system
  • user profile
  • privacy concern
  • homomorphic encryption
  • user privacy

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