More　and　more　sensitive　data　is　being　collected　and　processed　by　third　parties　for　data　mining　and　analysis,　in　order　to　solve　the　problem　of　how　to　achieve　a　certain　balance　between　privacy　protection　and　data　analysis　accuracy,　in　this　thesis,　a　personalized　local　differential　privacy　protection　method　based　on　feature　weights　is　proposed.　The　influence　weight　of　feature　attributes　on　data　analysis　is　taken　as　the　core　basis　of　the　whole　perturbation　method.　The　gradient　response　algorithm　K-RR　[1]　is　mainly　used　to　realize　random　disturbance　of　data.　However,　in　order　to　ensure　the　accuracy　of　subsequent　data　analysis　as　much　as　possible,　the　consideration　of　the　influence　of　feature　attributes　on　weight　size,　feature　attribute　dimension,　and　privacy　budget　allocation　is　introduced.　In　addition,　the　personalized　privacy　requirements　of　different　data　owners　are　fully　considered.　By　setting　the　total　privacy　budget　defined　by　data　owners　and　constructing　the　sensitive　attribute　set　defined　by　data　owners,　personalized　local　differential　privacy　protection　is　realized　based　on　the　influence　weight　of　attributes.　In　addition,　different　privacy　budget　loss　calculation　methods　are　constructed　for　different　attributes　based　on　the　influence　weight　of　characteristic　attributes　to　ensure　data　privacy.　©　2023　ACM.