J. Paranthaman

Performance Evaluation of K-Anonymized Data

Data mining provides tools to convert a large amount of knowledge data which is user relevant. But this process could return individual’s sensitive information compromising their privacy rights. So, based on different approaches, many privacy protection mechanism incorporated data mining techniques were developed. A widely used micro data protection concept is k-anonymity, proposed to capture the protection of a micro data table regarding re-identification of respondents which the data refers to. In this paper, the effect of the anonymization due to k-anonymity on the data mining classifiers is investigated. Naïve Bayes classifier is used for evaluating the anonymized and non-anonymized data.