With　the　widely　use　of　recommendation　systems　in　various　mobile　applications,　privacy　leakage　has　been　a　longstanding　threat,　for　which　many　researchers　have　come　up　with　a　great　number　of　methods　that　achieve　the　protective　effect　to　a　certain　extent.　However,　the　protection　scope　of　these　methods　is　limited,　especially　in　the　protection　of　original　data.　To　address　this　issue,　we　propose　a　data　perturbation　based　Rényi　differential　privacy　algorithm　to　protect　the　SVD　recommendation　model.　This　paper　uses　the　data　perturbation　method　to　perturb　the　original　training　dataset　in　the　data　preprocessing　stage,　then　leverages　the　perturbed　data　to　train　the　SVD　model,　and　the　unperturbed　data　is　used　as　a　test　set　to　verify　the　accuracy　of　the　model.　Compared　with　the　objective　perturbation,　gradient　perturbation,　and　output　perturbation,　the　data　perturbation　can　protect　a　broader　range　and　realize　the　corresponding　functions　of　the　other　three　perturbed　methods　by　using　the　post-processing　property　of　differential　privacy.　Experimental　results　show　that　the　proposed　method　can　effectively　protect　user　privacy,　improve　the　effectiveness　of　data,　and　generate　better　recommendation　results　without　seriously　affecting　the　accuracy　of　the　model.　©　2021　The　authors　and　IOS　Press.