Privacy Protection in Memory-Based Collaborative Filtering

  • Wim F. J. Verhaegh
  • Aukje E. M. van Duijnhoven
  • Pim Tuyls
  • Jan Korst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3295)


We discuss the issue of privacy protection in collaborative filtering, focusing on the commonly-used memory-based approach. We show that the two main steps in collaborative filtering, being the determination of similarities and the prediction of ratings, can be performed on encrypted profiles, thereby securing the users’ private data. We list a number of variants of the similarity measures and prediction formulas described in literature, and show for each of them how they can be computed using encrypted data only. Although we consider collaborative filtering in this paper, the techniques of comparing profiles using encrypted data only is much wider applicable.


Privacy Protection Collaborative Filter Encrypt Data Recommendation Algorithm Prediction Formula 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Wim F. J. Verhaegh
    • 1
  • Aukje E. M. van Duijnhoven
    • 2
  • Pim Tuyls
    • 1
  • Jan Korst
    • 1
  1. 1.Philips Research LaboratoriesEindhovenThe Netherlands
  2. 2.Technische Universiteit EindhovenEindhovenThe Netherlands

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