Privacy　becomes　a　more　serious　concern　in　applications　involving　microdata　such　as　medical　data　publishing　or　medical　data　mining.　Anonymization　methods　based　on　global　recoding　or　local　recoding　or　clustering　provide　privacy　protection　by　guaranteeing　that　each　released　record　will　be　indistinguishable　to　some　other　individual.　However,　such　methods　may　not　always　achieve　effective　anonymization　in　terms　of　analysis　workload　using　the　anonymized　data.　The　utility　of　attributes　has　not　been　well　considered　in　the　previous　methods.　This　paper　studies　the　problem　of　utility-based　anonymization　to　concentrate　on　attributes　order　sensitive　workload,　where　the　order　of　the　attributes　is　important　to　the　analysis　workload.　Based　on　the　multidimensional　anonymization　concept,　a　method　is　discussed　for　attributes　order　sensitive　utility-based　anonymization.　The　performance　study　using　public　data　sets　shows　that　the　efficiency　is　not　affected　by　the　attributes　order　processing.　©　by　Institute　of　Software,　the　Chinese　Academy　of　Sciences.　All　rights　reserved.