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Accuracy and Utility Balanced Privacy Preserving Classification Mining by Improving K-Anonymization

The data available is vast and data is being analyzed to improve businesses. This data analysis also contributes to society in different ways. Now there are new challenges to protect privacy of data. So, Privacy Preserving Data Mining (PPDM) techniques have evolved which protect the privacy of data while carrying out data analysis. Privacy Preserving Data Publishing (PPDP) is a part of PPDM which is a major research area. As part of PPDP several anonymization algorithms are proposed. Kanonymization is one among them. In this paper a new method for privacy preserving data mining is proposed which is better than applying k-anonymization alone. The present research work focuses on the approach which decreases the risk of various attacks and at the same time provides more utility of data.