An Efficient Approach for Statistical Anonymization Techniques for Privacy Preserving Data Mining

Abstract: Data mining has become more concerned with the rapid exploration of metadata in web and with the collection of information and analysis of data, violates the privacy preserving of private data. Privacy Preserving Data Mining is a research area concerned with the privacy driven from personally identifiable information when considered for data mining. With anonymity techniques over the web, it produced good results, across the system in world. Hence more techniques are introduced, to protect the security of sensitive data. In this paper, we provide statistical anonymization methods which can be applied for privacy preserving data mining. This paper also describes about anonymity models, major implementation ways and the strategies of anonymity algorithms.

Keywords: Privacypreserving, anonymization, perturbation, micro-aggregation, synthetic micro data