SVD Based Data Transformation Methods for Privacy Preserving Clustering

Provided by: International Journal of Computer Applications
Topic: Security
Format: PDF
Now-a-days 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.

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