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.


About Paul Resnick

Professor, University of Michigan School of Information Personal home page
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