Perturbation Based Privacy Preserving Slope One Predictors for Collaborative Filtering

摘要

The prediction of the rating that a user is likely to give to an item, can be derived from the ratings of other items given by other users, through collaborative filtering (CF). However, CF raises concerns about the privacy of the individual user's rating data. To deal with this, several privacy-preserving CF schemes have been proposed. However, they are all limited either in terms of efficiency or privacy when deployed on the cloud. Due to its simplicity, Lemire and MacLachlan's weighted Slope One predictor is very well suited to the cloud. Our key insight is that, the Slope One predictor, being an invertible affine transformation, is robust to certain types of noise. We exploit this fact to propose a random perturbation based privacy preserving collaborative filtering scheme. Our evaluation shows that the proposed scheme is both efficient and preserves privacy.