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Wednesday, June 14th, 2006

Time Event
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.
Peer Prediction Method with Reduced Payments
ACM EC 06. Jurca and Faltings

Work extends my "Peer Prediction" paper, written with Nolan Miller and Richard Zeckhauser, on eliciting honest reports, by comparing reports between people.

Automatically selects a scoring rule, with lower expected payments but still incentive compatible.

Has some mechanism for probabilitistically filtering out unusual ratings. I'll have to look at the paper to see the details of this.

Claims that the honest reporting equilibrium is evolutionarily stable, meaning that small coalitions can't attack it. Again, I'll have to take a look at this.

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