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Papers by naga lakshmi
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2011
Privacy preservation is a major concern when the application of data mining techniques to large r... more Privacy preservation is a major concern when the application of data mining techniques to large repositories of data consists of personal, sensitive and confidential information. Singular Value Decomposition (SVD) is a matrix factorization method, which can produces perturbed data by efficiently removing unnecessary information for data mining. In this paper two hybrid methods are proposed which takes the advantage of existing techniques SVD and geometric data transformations in order to provide better privacy preservation. Reflection data perturbation and scaling data perturbation are familiar geometric data transformation methods which retains the statistical properties in the dataset. In hybrid method one, SVD and scaling data perturbation are used as a combination to obtain the distorted dataset. In hybrid method two, SVD and reflection data perturbation methods are used as a combination to obtain the distorted dataset. The experimental results demonstrated that the proposed hyb...
International Journal of Computer Applications, 2013
Nowadays privacy issues are major concern for many government and other private organizations to ... more Nowadays privacy issues are major concern for many government and other private organizations to delve important information from large repositories of data. Privacy preserving clustering which is one of the techniques emerged to addresses the problem of extracting useful clustering patterns from distorted data without accessing the original data directly. In this paper two hybrid data transformation methods are proposed for privacy preserving clustering in centralized database environment based on Singular Value Decomposition (SVD). In hybrid method one, SVD and rotation data perturbation are used as a combination to obtain the distorted dataset. In hybrid method two, SVD and independent component analysis are used as a combination to obtain the distorted dataset. In SVD the data is analyzed in different perspectives to retain important information. Higher order statistics which contains more important information is utilized in independent component analysis. Experimental results demonstrate that the proposed methods are efficiently protects the private data of individuals and retains the important information for clustering analysis.
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2011
Privacy preservation is a major concern when the application of data mining techniques to large r... more Privacy preservation is a major concern when the application of data mining techniques to large repositories of data consists of personal, sensitive and confidential information. Singular Value Decomposition (SVD) is a matrix factorization method, which can produces perturbed data by efficiently removing unnecessary information for data mining. In this paper two hybrid methods are proposed which takes the advantage of existing techniques SVD and geometric data transformations in order to provide better privacy preservation. Reflection data perturbation and scaling data perturbation are familiar geometric data transformation methods which retains the statistical properties in the dataset. In hybrid method one, SVD and scaling data perturbation are used as a combination to obtain the distorted dataset. In hybrid method two, SVD and reflection data perturbation methods are used as a combination to obtain the distorted dataset. The experimental results demonstrated that the proposed hyb...
International Journal of Computer Applications, 2013
Nowadays privacy issues are major concern for many government and other private organizations to ... more Nowadays privacy issues are major concern for many government and other private organizations to delve important information from large repositories of data. Privacy preserving clustering which is one of the techniques emerged to addresses the problem of extracting useful clustering patterns from distorted data without accessing the original data directly. In this paper two hybrid data transformation methods are proposed for privacy preserving clustering in centralized database environment based on Singular Value Decomposition (SVD). In hybrid method one, SVD and rotation data perturbation are used as a combination to obtain the distorted dataset. In hybrid method two, SVD and independent component analysis are used as a combination to obtain the distorted dataset. In SVD the data is analyzed in different perspectives to retain important information. Higher order statistics which contains more important information is utilized in independent component analysis. Experimental results demonstrate that the proposed methods are efficiently protects the private data of individuals and retains the important information for clustering analysis.