Privacy-Preserving Data Mining using RDT Framework and C4.5

Komal N. Chouragade, Trupti H. Gurav

Abstract: In later years privacy preservation in data mining has turned into main problem and needs to be solved. In this paper, to deal with this advancement in privacy preserving data mining technology using highlighted approach of Random Decision Tree (RDT) Random Decision Tree gives better efficiency and information protection over Cryptographic procedure. Cryptography technique is extremely slow and in plausible to enable truly huge scale investigation to manage time of enormous data. Random Decision Tree is utilized for different data mining task like classification, multiple classifications. Privacy-preserving RDT used for both cryptographic technique and randomization which provide data privacy for some decision tree algorithm. In this algorithm we are using ID3 and C4.5 Decision tree algorithms. By using C4.5 to improve Random Decision tree is the main contribution of our work.

Keywords: Privacy-preserving, data mining, Classifier, decision Tree, ID3, C45