Cross-Domain　Recommendation　(CDR)　have　become　increasingly　appealing　by　leveraging　useful　information　to　tackle　the　data　sparsity　problem　across　domains.　Most　of　latest　CDR　models　assume　that　domain-shareable　user-item　information　(e.g.,　rating　and　review　on　overlapped　users　or　items)　are　accessible　across　domains.　However,　these　assumptions　become　impractical　due　to　the　strict　data　privacy　protection　policy.　In　this　paper,　we　propose　Reducing　Item　Discrepancy　(RidCDR)　model　on　solving　Privacy-Preserving　Cross-Domain　Recommendation　(PPCDR)　problem.　Specifically,　we　aim　to　enhance　the　model　performance　on　both　source　and　target　domains　without　overlapped　users　and　items　while　protecting　the　data　privacy.　We　innovatively　propose　private-robust　embedding　alignment　module　in　RidCDR　for　knowledge　sharing　across　domains　while　avoiding　negative　transfer　privately.　Our　empirical　study　on　Amazon　and　Douban　datasets　demonstrates　that　RidCDR　significantly　outperforms　the　state-of-the-art　models　under　the　PPCDR　without　overlapped　users　and　items.　Copyright　2024　by　the　author(s)