Fuzzy random decision tree (FRDT) framework for privacy preserving data mining

Abstract

Distributed data is enormously evolving now a day. Efficient classification models like Random Decision Trees (RDT) provide a way to classify the distributed data and also provide security to data at owner's site. Previous works on RDT along with privacy preserving declared that they are a better fit for privacy preserving data mining by generating models with low cost. In this paper we proposed a framework to generate models which use only effective RDTs along with cryptographic techniques to provide privacy for individual data in a collaborative environment. We applied fuzzy membership thresholds to verify the utility of an RDT. Instead of considering all the possible RDTs over a data set for calculating the classification result, the RDTs that are built with highly significant attributes are the only ones considered for finding the average of classification result. This model provides an efficient solution for privacy preserving knowledge discovery.