A practical system for privacy-preserving collaborative filtering

  • Chow R
  • Pathak M
  • Wang C
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Collaborative filtering is a widely-used technique in online services to
enhance the accuracy of a recommender system. This technique, however,
comes at the cost of users having to reveal their preferences, which has
undesirable privacy implications. We propose a collaborative filtering
system where the system does not observe the users' data and is still
able to provide useful recommendations. Compared to prior systems, our
emphasis is on building a practical system that can be reasonably used
by a large number of users. Our approach involves creating a primitive
to cluster similar users privately by modifying existing methods such as
Locality Sensitive Hashing. Another technique we use is artificial
ratings, as part of the process of privately predicting the rating for
an item within a particular cluster. We evaluate our scheme on the
Netflix Prize dataset, reporting the accuracy of our recommendations as
a function of the privacy provided.

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  • Richard Chow

  • Manas A. Pathak

  • Cong Wang

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