Research　on　the　anonymization　of　static　data　has　made　great　progress　in　recent　years.　Generalization　and　suppression　are　two　common　technologies　for　quasi-identifiers＇　anonymization.　However,　the　characteristics　of　data　streams,　such　as　potential　infinity　and　high　dynamicity,　make　the　anonymization　of　data　streams　different　from　the　anonymization　of　static　data.　The　methods　for　static　data　anonymization　cannot　be　directly　applied　to　anonymizing　data　streams.　In　this　paper,　a　novel　k-anonymization　approach　for　data　streams　based　on　clustering　is　proposed.　In　order　to　speed　up　the　anonymization　process　and　reduce　the　information　loss,　the　new　approach　scans　a　stream　in　one　turn　to　recognize　and　reuse　the　clusters　satisfying　the　k-anonymity　principle.　The　time　constraints　on　tuple　publication　and　cluster　reuse,　which　are　specific　to　data　streams,　are　considered　as　well.　Furthermore,　the　approach　is　improved　to　conform　to　the　l-diversity　principle.　The　experiments　conducted　on　the　real　datasets　show　that　the　proposed　methods　are　both　efficient　and　effective.　(C)　2013　Elsevier　B.V.　All　rights　reserved.