K-anonymity　privacy　protection　model　demonstrates　good　performance　in　privacy　protection　and,　has　been　widely　applied　in　such　scenarios　as　data　publishing,　location-based　services,　and　social　networks.　With　the　aim　of　ensuring　k-anonymity　to　conform　to　the　requirements　of　privacy　protection　with　improved　data　utilization,　this　study　proposes　a　k-anonymity　algorithm　based　on　central　point　clustering,　so　as　to　improve　the　quality　of　clustering　through　optimizing　the　selection　of　cluster　centroids,　leading　to　the　improvement　in　effectiveness　and　efficiency　of　k-anonymity.　After　clustering,　the　quasi-identifier　attributes　are　aligned　for　classification　and　generalization,　which　is　evaluated　using　appropriate　information　loss　metrics.　To　measure　the　distance　between　records　and　between　records　and　clusters,　this　study　also　establishes　a　definition　of　such　distance　that　is　positively　correlated　to　the　amount　of　information　that　is　lost　by　combining　the　characteristics　of　the　depth　and　width　of　the　generalization　hierarchy,　in　an　effort　to　improve　of　the　utility　of　the　algorithm.　The　exper-imental　results　show　that　the　proposed　algorithm　not　only　meets　the　basic　anonymity　requirements,　but　also　improves　data　utilization　compared　with　some　prevailing　algorithms.　©　2022,　University　of　Skovde.　All　rights　reserved.