Collaborative Filtering with Privacy

ACM EC ’06. Presentation on privacy-preserving collaborative filtering.

Previous approaches:

  • secure multi-party computation to compute eignevectors (Canny).
  • add noise to each rating

This paper shows that adding noise may not preserve as much privacy as you’ d like. If the noise for each rating is a random draw from the same distribution, and if there is a finite set of possible ratings, then you can make a pretty good backward inference about what the original ratings were. The basic idea is…

The solution in this paper is to have users add a variable amount of noise to their ratings, not the same draw for each item.

I haven’t had a chance to read the paper in detail yet, but it seems quite elegant. I hope I’ll be able to use it in my recommender systems course this fall, though the math may be too advanced.

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About Paul Resnick

Professor, University of Michigan School of Information Personal home page
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