  Federated recommendation addresses the data silo and privacy problems
altogether for recommender systems. Current federated recommender systems
mainly utilize cryptographic or obfuscation methods to protect the original
ratings from leakage. However, the former comes with extra communication and
computation costs, and the latter damages model accuracy. Neither of them could
simultaneously satisfy the real-time feedback and accurate personalization
requirements of recommender systems. In this paper, we proposed federated
masked matrix factorization (FedMMF) to protect the data privacy in federated
recommender systems without sacrificing efficiency and effectiveness. In more
details, we introduce the new idea of personalized mask generated only from
local data and apply it in FedMMF. On the one hand, personalized mask offers
protection for participants' private data without effectiveness loss. On the
other hand, combined with the adaptive secure aggregation protocol,
personalized mask could further improve efficiency. Theoretically, we provide
security analysis for personalized mask. Empirically, we also show the
superiority of the designed model on different real-world data sets.
