We　consider　the　problem　of　local　privacy　where　actions　are　subsets　of　a　ground　multiset　and　expectation　rewards　are　modeled　by　a　1-decomposable　monotone　submodular　function.　For　the　DR-submodular　maximization　problem　under　a　polymatroid　constraint,　Soma　and　Yoshida　[26]　provide　a　continuous　greedy　algorithm　for　no-privacy　setting.　In　this　paper,　we　obtain　the　first　differentially　private　algorithm　for　DR-submodular　maximization　subject　to　a　polymatroid　constraint.　Our　algorithm　achieves　a　(1　-　1/e　-　O(rho))-approximation　with　a　little　loss　and　runs　in　O(n(2)r(3)/rho(6)　.log(3)(n/rho).log(3)r　+　n(8))　times　where　r　is　the　rank　of　the　base　polymatroid　and　n　is　the　size　of　ground　set.　Along　the　way,　we　analyze　the　utility　and　privacy　of　our　algorithm.　A　concrete　experiment　to　simulate　the　privacy　Uber　pickups　location　problem　is　provided,　and　our　algorithm　performs　well　within　the　agreed　range.