@inproceedings{8be15b17f0434cd3acbab8746dbe842e,
title = "Differential private random forest",
abstract = "Organizations be it private or public often collect personal information about an individual who are their customers or clients. The personal information of an individual is private and sensitive which has to be secured from data mining algorithm which an adversary may apply to get access to the private information. In this paper we have consider the problem of securing these private and sensitive information when used in random forest classifier in the framework of differential privacy. We have incorporated the concept of differential privacy to the classical random forest algorithm. Experimental results shows that quality functions such as information gain, max operator and gini index gives almost equal accuracy regardless of their sensitivity towards the noise. Also the accuracy of the classical random forest and the differential private random forest is almost equal for different size of datasets. The proposed algorithm works for datasets with categorical as well as continuous attributes.",
author = "Abhijit Patil and Sanjay Singh",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 3rd International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014 ; Conference date: 24-09-2014 Through 27-09-2014",
year = "2014",
month = jan,
day = "1",
doi = "10.1109/ICACCI.2014.6968348",
language = "English",
series = "Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2623--2630",
editor = "Comer, {Douglas E.} and Peter Mueller and Bhawna Mallick and Sougata Mukherjea and Thampi, {Sabu M.} and Dilip Krishnaswamy and Axel Sikora",
booktitle = "Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014",
address = "United States",
}