As　people　become　increasingly　reliant　on　the　Internet,　securely　storing　and　publishing　private　data　has　become　an　important　issue.　In　real　life,　the　release　of　graph　data　can　lead　to　privacy　breaches,　which　is　a　highly　challenging　problem.　Although　current　research　has　addressed　the　issue　of　identity　disclosure,　there　are　still　two　challenges:　First,　the　privacy　protection　for　large-scale　datasets　is　not　yet　comprehensive;　Second,　it　is　difficult　to　simultaneously　protect　the　privacy　of　nodes,　edges,　and　attributes　in　social　networks.　To　address　these　issues,　this　paper　proposes　a　(k,　t)-graph　anonymity　algorithm　based　on　enhanced　clustering.　The　algorithm　uses　k-means++　clustering　for　k-anonymity　and　t-closeness　to　improve　k-anonymity.　We　evaluate　the　privacy　and　efficiency　of　this　method　on　two　datasets　and　achieved　good　results.　This　research　is　of　great　significance　for　addressing　the　problem　of　privacy　breaches　that　may　arise　from　the　publication　of　graph　data.