Open AccessMin Max Normalization Based Data Perturbation Method for Privacy Protection
Author(s) -
Yogendra Kumar Jain,
Santosh Kumar Bhandare
Publication year - 2013
Publication title -
international journal of computer and communication technology
Language(s) - English
Resource type - Journals
eISSN - 2231-0371
pISSN - 0975-7449
DOI - 10.47893/ijcct.2013.1201
Subject(s) - normalization (sociology) , data mining , computer science , confidentiality , privacy protection , information privacy , database normalization , private information retrieval , perturbation (astronomy) , data protection act 1998 , computer security , pattern recognition (psychology) , artificial intelligence , sociology , anthropology , physics , quantum mechanics
Data mining system contain large amount of private and sensitive data such as healthcare, financial and criminal records. These private and sensitive data can not be share to every one, so privacy protection of data is required in data mining system for avoiding privacy leakage of data. Data perturbation is one of the best methods for privacy preserving. We used data perturbation method for preserving privacy as well as accuracy. In this method individual data value are distorted before data mining application. In this paper we present min max normalization transformation based data perturbation. The privacy parameters are used for measurement of privacy protection and the utility measure shows the performance of data mining technique after data distortion. We performed experiment on real life dataset and the result show that min max normalization transformation based data perturbation method is effective to protect confidential information and also maintain the performance of data mining technique after data distortion.