User Consented Federated Recommender System Against Personalized
  Attribute Inference Attack
Qi Hu Yangqiu Song
Recommender systems can be privacy-sensitive. To protect users' private
historical interactions, federated learning has been proposed in distributed
learning for user representations. Using federated recommender (FedRec)
systems, users can train a shared recommendation model on local devices and
prevent raw data transmissions and collections. However, the recommendation
model learned by a common FedRec may still be vulnerable to private information
leakage risks, particularly attribute inference attacks, which means that the
attacker can easily infer users' personal attributes from the learned model.
Additionally, traditional FedRecs seldom consider the diverse privacy
preference of users, leading to difficulties in balancing the recommendation
utility and privacy preservation. Consequently, FedRecs may suffer from
unnecessary recommendation performance loss due to over-protection and private
information leakage simultaneously. In this work, we propose a novel
user-consented federated recommendation system (UC-FedRec) to flexibly satisfy
the different privacy needs of users by paying a minimum recommendation
accuracy price. UC-FedRec allows users to self-define their privacy preferences
to meet various demands and makes recommendations with user consent.
Experiments conducted on different real-world datasets demonstrate that our
framework is more efficient and flexible compared to baselines.