Towards user-oriented privacy for recommender system data: A personalization-based approach to gender obfuscation for user profiles

In this paper, we propose a new privacy solution for the data used to train a recommender system, i.e., the user–item matrix. The user–item matrix contains implicit information, which can be inferred using a classifier, leading to potential privacy violations. Our solution, called Personalized Blurring (PerBlur), is a simple, yet effective,...

BlUrM(or)e: Revisiting gender obfuscation in the user-item matrix

Past research has demonstrated that removing implicit gender information from the user-item matrix does not result in substantial performance losses. Such results point towards promising solutions for protecting users’ privacy without compromising prediction performance, which are of particular interest in multistakeholder environments. Here,...