Anonymizing classification data for preserving privacy

Abstract

Classification of data with privacy preservation is a fundamental problem in privacy preserving data mining. The privacy goal requires concealing the sensitive information that may identify certain individuals breaching their privacy, whereas the classification goal requires to accurately classifying the data. One way to achieve both is to anonymize the dataset that contains the sensitive information of individuals before getting it released for data analysis. Microaggregation is an efficient privacy preservation technique used by statistical disclosure control community as well as data mining community to anonymize a dataset. It naturally satisfies k-anonymity without resorting to generalisations or suppression of data. In this paper we propose a new method named Microaggregation based Classification Tree (MiCT). In MiCT method data are perturbed prior to its classification and we use tree properties to achieve the objective of privacy preserving classification of data. To evaluate the effectiveness of the proposed method we have conducted experiments on real life data and proved that our method provides improved classification accuracy by preserving privacy.