30th Annual ACM Symposium on Applied Computing, SAC 2015, Salamanca, Spain, 13 - 17 April 2015, vol.13-17-April-2015, pp.901-907
Copyright 2015 ACM.Privacy-preserving collaborative filtering methods promise to preserve privacy of individuals. In general, privacy has two aspects, preserving the rating values of users and masking who rated which items. In this study, we analyze a privacy-preserving collaborative filtering method for binary data referred to as randomized response technique. We develop a method targeting the second aspect of privacy to discover fake binary ratings using auxiliary and public information.