Recently,　dual-target　cross-domain　recommendation　(DTCDR)　has　been　proposed　to　alleviate　the　data　sparsity　problem　by　sharing　the　common　knowledge　across　domains　simultaneously.　However,　existing　methods　often　assume　that　personal　data　containing　abundant　identifiable　information　can　be　directly　accessed,　which　results　in　a　controversial　privacy　leakage　problem　of　DTCDR.　To　this　end,　we　introduce　the　P2DTR　framework,　a　novel　approach　in　DTCDR　while　protecting　private　user　information.　Specifically,　we　first　design　a　novel　inter-client　knowledge　extraction　mechanism,　which　exploits　the　private　set　intersection　algorithm　and　prototype-based　federated　learning　to　enable　collaboratively　modeling　among　multiple　users　and　a　server.　Furthermore,　to　improve　the　recommendation　performance　based　on　the　extracted　common　knowledge　across　domains,　we　proposed　an　intra-client　enhanced　recommendation,　consisting　of　a　constrained　dominant　set　(CDS)　propagation　mechanism　and　dual-recommendation　module.　Extensive　experiments　on　real-world　datasets　validate　that　our　proposed　P2DTR　framework　achieves　superior　utility　under　a　privacy-preserving　guarantee　on　both　domains.