How to Profile Privacy-Conscious Users in Recommender Systems
This addresses privacy concerns for users in recommender systems, though it is incremental as it builds on existing matrix factorization methods.
The paper tackles the cold-start problem in matrix factorization-based recommender systems by designing a protocol that allows privacy-conscious users to learn their profiles and predict ratings without revealing personal information, achieving security against semi-honest adversaries in the standard model.
Matrix factorization is a popular method to build a recommender system. In such a system, existing users and items are associated to a low-dimension vector called a profile. The profiles of a user and of an item can be combined (via inner product) to predict the rating that the user would get on the item. One important issue of such a system is the so-called cold-start problem: how to allow a user to learn her profile, so that she can then get accurate recommendations? While a profile can be computed if the user is willing to rate well-chosen items and/or provide supplemental attributes or demographics (such as gender), revealing this additional information is known to allow the analyst of the recommender system to infer many more personal sensitive information. We design a protocol to allow privacy-conscious users to benefit from matrix-factorization-based recommender systems while preserving their privacy. More precisely, our protocol enables a user to learn her profile, and from that to predict ratings without the user revealing any personal information. The protocol is secure in the standard model against semi-honest adversaries.