Abstract
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 work, 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.
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Yang, L. et al. (2023). Practical and Secure Federated Recommendation with Personalized Mask. In: Goebel, R., Yu, H., Faltings, B., Fan, L., Xiong, Z. (eds) Trustworthy Federated Learning. FL 2022. Lecture Notes in Computer Science(), vol 13448. Springer, Cham. https://doi.org/10.1007/978-3-031-28996-5_3
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