Privacy Preservation in Data Mining Using Hybrid Approach

Abstract

Data sharing has become common now days and there is an exponential growth in the amount of information. Data mining is the extraction of large amount of useful information from massive databases. Data mining is considered as the core of the KDD (Knowledge Discovery in Data mining) process. Privacy is at risk when the personal and sensitive information about a person is leaked and hence privacy must be preserved. Privacy Preservation In Data Mining (PPDM) is the area in data mining which protects the sensitive data or information from unauthorized disclosure. There are many privacy preserving algorithms like k-anonymity, l-diversity, t-closeness, slicing existing to protect the privacy of data. In this paper, we propose a new approach for preserving the privacy of data which is the hybrid approach. Our approach which is the combination of task independent privacy preservation and k-anonymization efficiently preserves the sensitive data and provides high accuracy when compared to the accuracy provided by the above mentioned algorithms when applied separately on the datasets.