Privacy Preserving for Feature Selection in Data Mining Using Centralized Network

This paper proposed a feature selection with privacy preservation in centralized network. Data can be preserved for privacy by perturbation technique as alias name. In centralized data evaluation, it makes data classification and feature selection for data mining decision model which make the structural information of model in this paper. The application of gain ratio technique for better performance of feature selection has taken to perform the centralized computational task. All features don‟t need to preserve the privacy for confidential data for best model. The chi-square testing has taken for the classification of data by centralized data mining model using own processing unit. The alias data model for privacy preserving data mining has taken to develop the data mining technique to make best model without violating the privacy individuals. The proposed process of data miner task has made best feature selection and two type experimental tests have taken in this paper.

[1]  Hillol Kargupta,et al.  Distributed probabilistic inferencing in sensor networks using variational approximation , 2008, J. Parallel Distributed Comput..

[2]  Hillol Kargupta,et al.  A Scalable Local Algorithm for Distributed Multivariate Regression , 2008 .

[3]  Dawn Xiaodong Song,et al.  Privacy-Preserving Set Operations , 2005, CRYPTO.

[4]  Ton de Waal,et al.  Statistical Disclosure Control in Practice , 1996 .

[5]  Keith B. Frikken.  Secure multiparty computation , 2010 .

[6]  Chris Clifton,et al.  Privacy-Preserving Decision Trees over Vertically Partitioned Data , 2005, DBSec.

[7]  Alexandre V. Evfimievski,et al.  Information sharing across private databases , 2003, SIGMOD '03.

[8]  Nabil R. Adam,et al.  Security-control methods for statistical databases: a comparative study , 1989, ACM Comput. Surv..

[9]  Sheng Zhong,et al.  Anonymity-preserving data collection , 2005, KDD '05.

[10]  Chris Clifton,et al.  Privacy-preserving distributed mining of association rules on horizontally partitioned data , 2004, IEEE Transactions on Knowledge and Data Engineering.

[11]  Ran Wolff,et al.  A Local Facility Location Algorithm for Large-scale Distributed Systems , 2007, Journal of Grid Computing.

[12]  A. Yao,et al.  Fair exchange with a semi-trusted third party (extended abstract) , 1997, CCS '97.

[13]  Dorothy E. Denning,et al.  Cryptography and Data Security , 1982 .

[14]  Benny Pinkas,et al.  Efficient Private Matching and Set Intersection , 2004, EUROCRYPT.

[15]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[16]  Chris Clifton,et al.  Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data , 2004, SDM.

[17]  Jaideep Vaidya,et al.  Privacy Preserving Naive Bayes Classifier for Horizontally Partitioned Data , 2003 .

[18]  Hillol Kargupta,et al.  A Scalable Local Algorithm for Distributed Multivariate Regression , 2008, Stat. Anal. Data Min..

[19]  Kannan Balasubramanian,et al.  Secure Multiparty Computation , 2011, Encyclopedia of Cryptography and Security.

[20]  Chris Clifton,et al.  Privacy-Preserving Distributed k-Anonymity , 2005, DBSec.