IJREAS Volume 3, Issue 3 (March 2013) ISSN: 2249-3905 International Journal of Research in Engineering & Applied Sciences 34 http://www.euroasiapub.org PRIVACY PRESERVING DECISION TREE LEARNING USING UNREALIZED DATA SETS M. R. Pawar* Mampi Bhowmik* ABSTRACT In recent years, advances in hardware technology have led to an increase in the capability to store and record personal data about consumers and individuals. This has led to concerns that the personal data may be misused for a variety of purposes. In order to alleviate these concerns, a number of techniques have recently been proposed in order to perform the data mining tasks in a privacy-preserving way. These techniques for performing privacy- preserving data mining are drawn from a wide array of related topics such as data mining, cryptography and information hiding. Privacy preservation is important for machine learning and data mining, but measures designed to protect private information often result in a trade-off: reduced utility of the training samples. This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of the privacy of collected data samples in cases where information from the sample database has been partially lost. This approach converts the original sample data sets into a group of unreal data sets, from which the original samples cannot be reconstructed without the entire group of unreal data sets. Meanwhile, an accurate decision tree can be built directly from those unreal data sets. This novel approach can be applied directly to the data storage as soon as the first sample is collected. The approach is compatible with other privacy preserving approaches, such as cryptography, for extra protection. Keywords: cryptography, data mining, decision tree, security and privacy protection. *Department of Computer Technology, K. K. Wagh Polytechnic, Nashik