Publishing　the　original　form　of　data,　typically　the　kind　of　data　which　contains　personal　information,　will　violate　individual　privacy.　One　challenge　problem　is　how　to　release　privacy　preserved　data　while　it　is　still　useful.　This　paper　studies　partition-based　algorithms　for　privacy　preserving　data　publishing.　Such　kind　of　algorithms　sets　total　orders　over　each　attribute　domain　of　a　given　table,　and　maps　each　tuple　into　a　multidimensional　space.　Then　finding　an　anonymized　form　of　the　original　data　equals　to　finding　a　partition　of　a　corresponding　multidimensional　rectangular　box.　If　different　regions　does　not　intersect　with　each　other,　a　partition　is　called　a　strict　partition;　Otherwise,　it　is　a　called　a　relaxed　partition.　This　paper　proves　that　the　data　quality　and　utility　of　a　given　strict　partition　can　be　improved　by　further　partitioning　it　into　smaller　but　intersecting　subregions.　Then,　combining　advanced　relaxed　partition　technique　and　Strict　Mondrian　Algorithm(the　state-of-the-art　strict　partition-based　algorithm),　we　design　a　Hybrid　Algorithm.　Through　experiments　on　the　famous　adult　dataset,　we　show　that　the　anonymized　result　of　the　Hybrid　Algorithm　is　better　than　the　solutions　produced　by　Strict　Mondrian　and　two　advanced　relaxed　partition-based　algorithms　according　to　existing　quality　and　utility　evaluation　metrics.　©　2010　IEEE.