A Survey of Random Decision Tree Framework Privacy Preserving Data Mining

Vina M. Lomte, Hemlata B. Deorukhakar

Abstract: Data mining with data privacy and data utility has been emerged to manage distributed data efficiently. In this paper, to deal with this advancement in privacy preserving data mining technology using accentuate approach of Random Decision Tree (RDT). Random Decision Tree provides better efficiency and data privacy than Cryptographic technique. Cryptographic technique is too slow and infeasible to enable truly large scale analytics to manage era of big data. Random Decision Tree is used for multiple data mining task like classification, regression, ranking, and multiple classifications. Privacy preserving RDT uses both randomization and cryptographic technique which provide data privacy for some decision tree based learning task.

Keywords: Privacy preserving RDT, Data mining, Encryption, Decryption, Cryptography technique

How to Cite?: Vina M. Lomte, Hemlata B. Deorukhakar, "A Survey of Random Decision Tree Framework Privacy Preserving Data Mining", Volume 3 Issue 11, November 2014, International Journal of Science and Research (IJSR), Pages: 3127-3134, https://www.ijsr.net/getabstract.php?paperid=OCT141587, DOI: https://dx.doi.org/10.21275/OCT141587

Download Citation: APA | MLA | BibTeX | EndNote | RefMan