Singular value decomposition based data distortion strategy for privacy protection

Authors:
Shuting Xu;Jun Zhang;Dianwei Han;Jie Wang
Affiliations:
Department of Computer Information Systems, Virginia State University, 23806, Petersburg, VA, USA;Department of Computer Science, University of Kentucky, 40506-0046, Lexington, KY, USA;Department of Computer Science, University of Kentucky, 40506-0046, Lexington, KY, USA;Department of Computer Science, University of Kentucky, 40506-0046, Lexington, KY, USA
Venue:
Knowledge and Information Systems
Year:
2006

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Abstract

Privacy-preserving is a major concern in the application of data mining techniques to datasets containing personal, sensitive, or confidential information. Data distortion is a critical component to preserve privacy in security-related data mining applications, such as in data mining-based terrorist analysis systems. We propose a sparsified Singular Value Decomposition (SVD) method for data distortion. We also put forth a few metrics to measure the difference between the distorted dataset and the original dataset and the degree of the privacy protection. Our experimental results using synthetic and real world datasets show that the sparsified SVD method works well in preserving privacy as well as maintaining utility of the datasets.