Deriving Private Information from Randomly Perturbed Ratings

Collaborative filtering techniques have become popular in the past several years as an effective way to help people deal with information overload. An important security concern in traditional recommendation systems is that users disclose information that may compromise their individual privacy when providing ratings. Randomized perturbation schemes have been proposed to disguise user ratings while still producing accurate recommendations. However, recent research has suggested that perturbation schemes might not be able to preserve privacy as much as has been believed. We propose two data reconstruction methods that derive original private information from disguised data in existing perturbation collaborative filtering schemes. One method is based on k-means clustering and the other uses singular value decomposition (SVD). We have conducted theoretical and experimental analysis on the difference between original data and reconstructed data. Our experiments show that both methods can derive a considerable amount of original information. This study helps to determine an empirical trade-off between recommendation accuracy and user privacy in perturbation schemes.