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.