This　paper　considers　the　problem　of　privacy　preserving　transaction　data　publishing.　Transaction　data　are　usually　useful　for　data　mining.　While　it　is　high-dimensional　data,　traditional　anonymization　techniques　such　as　generalization　and　suppression　are　not　suitable.　In　this　paper,　we　present　a　novel　technique　based　on　anatomy　technique　and　propose　a　simple　linear-time　anonymous　algorithm　that　meets　the　l-diversity　requirement.　The　simulation　experiments　on　real　datasets　and　the　results　of　association　rules　mining　on　the　anonymous　transaction　data　showed　that　our　algorithm　can　safely　and　efficiently　preserve　the　privacy　in　transaction　data　publication,　while　ensuring　high　utility　of　the　released　data.　©2010　IEEE.