Correlation based anonymization using generalization and suppression for disclosure problems

Abstract

Huge volume of detailed personal data is regularly collected and sharing of these data is proved to be beneficial for data mining application. Data that include shopping habits, criminal records, credit records and medical history are very necessary for an organization to perform analysis and predict the trends and patterns, but it may prevent the data owners from sharing the data because of many privacy regulations. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accurate data mining result. In this paper k-Anonymity based approach is used to provide privacy to individual data by masking the attribute values using generalization and suppression. Due to some drawbacks of the existing model, it needs to be modified to fulfill the goal. Proposed model tries to prevent data disclosure problem by using correlation coefficient which estimates amount of correlation between attributes and helps to automate the attribute selection process for generalization and suppression. The main aim of proposed model is to increase the Privacy Gain and to maintain the accuracy of the data after anonymization.