Diffusion　models　and　their　variants　have　achieved　high-quality　image　generation　without　adversarial　training.　These　algorithms　provide　new　ideas　for　data　shortages　in　some　fields.　But　the　diffusion　model　also　faces　the　same　problem　as　other　generative　models:　the　learned　probability　density　function　will　retain　the　characteristics　of　the　training　samples,　which　means　that　the　high　complexity　of　the　deep　network　will　make　the　model　easily　remember　the　training　samples.　When　a　diffusion　model　is　applied　to　sensitive　datasets,　the　distribution　the　model　focuses　on　may　reveal　private　information,　and　the　security　concerns　described　above　become　more　pronounced.　To　address　this　challenge,　this　paper　proposes　a　privacy　diffusion　model　named　DPDM　(Differentially　Private　Denoise　Diffusion　Probability　Models)　that　satisfies　differential　privacy　by　adding　appropriate　noise　to　the　gradient　during　the　training.　Besides,　this　paper　adopts　a　series　of　optimization　strategies　to　improve　model　performance　and　training　speed　such　as　adaptive　gradient　clipping　threshold　and　dynamic　decay　learning　rate.　Through　the　evaluation　and　analysis　of　the　benchmark　dataset,　it　is　found　that　the　attempt　in　this　paper　has　promising　usability,　and　the　synthetic　data　has　better　performance.　Author