Membership Inference Attacks Against Latent Factor Model. (arXiv:2301.03596v1 [cs.CR])

The advent of the information age has led to the problems of information
overload and unclear demands. As an information filtering system, personalized
recommendation systems predict users' behavior and preference for items and
improves users' information acquisition efficiency. However, recommendation
systems usually use highly sensitive user data for training. In this paper, we
use the latent factor model as the recommender to get the list of recommended
items, and we representing users from relevant items Compared with the
traditional member …

acquisition age attacks data demands efficiency factor information led overload predict problems system systems training user data