Novel Approaches for Privacy Preserving Data Mining in K-Anonymity Model P (original) (raw)

In privacy preserving data mining, anonymization based approaches have been used to preserve the privacy of an individual. Existing literature addresses various anonymization based approaches for preserving the sensitive private information of an individual. The k-anonymity model is one of the widely used anonymization based approach. However, the anonymization based approaches suffer from the issue of information loss. To minimize the information loss various state-of-the-art anonymization based clustering approaches viz. Greedy k-member algorithm and Systematic clustering algorithm have been proposed. Among them, the Systematic clustering algorithm gives lesser information loss. In addition, these approaches make use of all attributes during the creation of an anonymized database. Therefore, the risk of disclosure of sensitive private data is higher via publication of all the attributes. In this paper, we propose two approaches for minimizing the disclosure risk and preserving the...