Decision Tree Classifier for Privacy Preservation
Tejaswini S. Pawar, Snehal Kamlapur · 2013
Abstract: In recent year’s privacy preservation in data mining has become an important issue. A new class of data mining method called privacy preserving data mining algorithm has been developed. The aim of these algorithms is to protect the sensitive information in data while extracting knowledge from large amount of data. We focus the general classification in a secured manner and introduce a privacy-preserving decision tree classifier using C4.5 algorithm. The entire original dataset is replaced by unreal dataset for preserving the privacy via dataset complementation. This novel approach can be applied directly to the data storage as soon as the first sample is collected and applied at any time during the data collection process. 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. An accurate decision tree can be built directly from those unreal data sets. This paper discusses dataset complementation approach for converting sample dataset into sanitized or altered dataset. It covers system architecture and mathematical model of decision tree classifier for preserving privacy via dataset complementation.