Most　of　the　previous　works　on　k-anonymization　focused　on　one-time　release　of　data.　However,　data　is　often　released　continuously　to　serve　various　information　purposes　in　reality.　The　purpose　of　this　study　is　to　develop　an　effective　solution　for　the　republication　of　incremental　datasets.　First,　we　analyze　several　possible　generalizations　in　the　anonymization　for　incremental　updates　and　propose　an　important　monotonic　generalization　principle　that　effectively　prevents　privacy　breach　in　re-publication.　Based　on　the　monotonic　generalization　principle,　we　then　propose　a　partitioning　based　algorithm　for　republication,　which　can　securely　anonymize　a　continuously　growing　dataset　in　an　efficient　manner　while　assuring　high　data　quality.　The　effectiveness　of　our　approach　is　confirmed　by　extensive　experiments　with　real　data.　©　2008　IEEE.