As　a　social　information　product,　the　privacy　and　usability　of　high-dimensional　data　are　the　core　issues　in　the　field　of　privacy　protection.　Feature　selection　is　a　commonly　used　dimensionality　reduction　processing　technique　for　high-dimensional　data.　Some　feature　selection　methods　only　process　some　of　the　features　selected　by　the　algorithm　and　do　not　take　into　account　the　information　associated　with　the　selected　features,　resulting　in　the　usability　of　the　final　experimental　results　not　being　high.　This　paper　proposes　a　hybrid　method　based　on　feature　selection　and　a　cluster　analysis　to　solve　the　data　utility　and　privacy　problems　of　high-dimensional　data　in　the　actual　publishing　process.　The　proposed　method　is　divided　into　three　stages:　(1)　screening　features;　(2)　analyzing　the　clustering　of　features;　and　(3)　adaptive　noise.　This　paper　uses　the　Wisconsin　Breast　Cancer　Diagnostic　(WDBC)　database　from　UCI＇s　Machine　Learning　Library.　Using　classification　accuracy　to　evaluate　the　performance　of　the　proposed　method,　the　experiments　show　that　the　original　data　are　processed　by　the　algorithm　in　this　paper　while　protecting　the　sensitive　data　information　while　retaining　the　contribution　of　the　data　to　the　diagnostic　results.