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Public and private organizations are collecting personal data regarding day to day life of\udindividuals and accumulating them in large databases. Data mining techniques may be applied\udto such databases to extract useful hidden knowledge. Releasing the databases for data mining\udpurpose may lead to breach of individual privacy. Therefore the databases must be protected\udthrough means of privacy preservation techniques before releasing them for data mining\udpurpose. Microaggregation is a privacy preservation technique used by statistical disclosure\udcontrol community as well as data mining community for microdata protection. The Maximum\uddistance to Average Vector (MDAV) is a very popular multivariate fixed-size microaggregation\udtechnique studied by many researchers. The principal goal of such techniques is to preserve\udprivacy without much information loss. In this paper we propose a variable-size, improved\udMDAV technique having low information loss. \u

MDAV2K: A Variable-Size Microaggregation Technique for Privacy Preservation