Privacy-preserving collaborative filtering using randomized perturbation techniques

Collaborative filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. To conduct collaborative filtering, data from customers are needed. However, collecting high quality data from customers is not an easy task because many customers are so concerned about their privacy that they might decide to give false information. We propose a randomized perturbation (RP) technique to protect users' privacy while still producing accurate recommendations.

1. Introduction

With the amount of the information available for individuals growing steadily, information overload has become a major problem for users. To make the information to serve users better, information filtering and recommendation schemes become more and more important. Collaborative filtering (CF) is a recent technique for such filtering and recommendation purposes.