Абстрактный

Adaptive Random Decision Tree: A New Approach for Data Mining with Privacy Preserving

Hemlata B. Deorukhakar,Pradnya Kasture

Now a day’s fastest growing field data mining with privacy preserving is essential for fast development of high dimensional data and to manage that data efficiently while preserving privacy. In this paper, to deal with distributed data in privacy preserving data mining technology using classification approach of Adaptive Random Decision Tree. Privacy preserving ARDT uses ID3 and Boosting within RDT with privacy preserving framework to provide better performance than existing system. In existing system, cryptography based technique is still too slow to be effective for managing distributed data. Random Decision Tree with data privacy is generating equivalent and accurate model but it also slow in computational time when distributed data grows. Privacy preserving ARDT handles distributed data efficiently. Privacy preserving ARDT provides better accuracy with data mining while preserving data privacy and reduces the computation time as compared to RDT with privacy preserving framework.