Cross-Domain　Recommendation　(CDR)　has　been　popularly　studied　for　solving　the　data　sparsity　problem　via　leveraging　rich　knowledge　from　the　auxiliary　domain.　Most　of　the　current　CDR　models　assume　that　user-item　ratings/reviews　are　both　accessible　during　the　training　procedure.　However,　it　is　impractical　nowadays　due　to　the　strict　data　privacy　protection　policy.　In　this　paper,　we　focus　on　the　Privacy-Preserving　Cross-Domain　Recommendation　problem　(PPCDR).　Although　some　previous　approaches　have　investigated　the　problems,　they　always　fail　to　effectively　utilize　the　rating　and　review　for　user　preference　modeling.　What　is　worse,　they　separate　the　processes　of　privacy　data　modeling　and　user-item　collaborative　filtering,　leading　to　suboptimal　solutions.　To　fill　this　gap,　we　propose　the　Differentially　Private　Sparse　Mapping　Recommendation　model　(DPSMRec),　an　end-to-end　cross-domain　recommendation　framework　for　solving　the　PPCDR　problem.　DPSMRec　includes　three　main　modules,　i.e.,　source　embedding　generation　module,　target　rating　prediction　module,　and　differentially　private　sparse　mapping　module.　Specifically,　the　source　embedding　generation　module　and　target　rating　prediction　module　are　set　to　exploit　the　ratings　and　review　in　each　domain.　The　differentially　private　sparse　mapping　module　aims　to　transfer　knowledge　among　the　overlapped　users　via　Wasserstein　distance　privately.　To　obtain　more　accurate　mapping　solutions,　we　further　propose　differentially　private　enhanced　sparse　optimal　transport　via　fused　Gromov-Wasserstein　distance　which　can　consider　both　structure　and　semantic　information　among　the　users　across　domains.　Our　empirical　study　on　Amazon　and　Douban　datasets　demonstrates　that　DPSMRec　significantly　outperforms　the　state-of-the-art　models　under　the　PPCDR　setting.　©　2023　ACM.