S. K. Chettri¹ and B. Borah², ¹Saint Mary's College, India and ²Tezpur University, India

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

Privacy preservation, data mining, microaggregation, variable-size, information loss.