In　order　to　prevent　the　disclosure　of　sensitive　information　and　protect　users＇　privacy,　the　generalization　and　suppression　of　technology　is　often　used　to　anonymize　the　quasi-identifiers　of　the　data　before　its　sharing.　Data　streams　are　inherently　infinite　and　highly　dynamic　which　are　very　different　from　static　datasets,　so　that　the　anonymization　of　data　streams　needs　to　be　capable　of　solving　more　complicated　problems.　The　methods　for　anonymizing　static　datasets　cannot　be　applied　to　data　streams　directly.　In　this　paper,　an　anonymization　approach　for　data　streams　is　proposed　with　the　analysis　of　the　published　anonymization　methods　for　data　streams.　This　approach　scans　the　data　only　once　to　recognize　and　reuse　the　clusters　that　satisfy　the　anonymization　requirements　for　speeding　up　the　anonymization　process.　Experimental　results　on　the　real　dataset　show　that　the　proposed　method　can　reduce　the　information　loss　that　is　caused　by　generalization　and　suppression　and　also　satisfies　the　anonymization　requirements　and　has　low　time　and　space　complexity.　©　2013　ISCAS.