A novel data distortion approach via selective SSVD for privacy protection

Authors:
Jie Wang;Jun Zhang;Shuting Xu;Weijun Zhong
Affiliations:
Laboratory for High Performance Scientific Computing and Computer Simulation, Department of Computer Science, University of Kentucky, Lexington, KY 40506 0046, USA.;Laboratory for High Performance Scientific Computing and Computer Simulation, Department of Computer Science, University of Kentucky, Lexington, KY 40506 0046, USA.;Department of Computer Information Systems, Virginia State University, Petersburg, VA 23806, USA.;Department of Management Sciences and Engineering, Southeast University, Nanjing, 210096, P.R. China
Venue:
International Journal of Information and Computer Security
Year:
2008

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Abstract

Data privacy preservation has become one of the major concerns in the design of practical data-mining applications. In this paper, a novel data distortion approach based on structural partition and Sparsified Singular Value Decomposition (SSVD) technique is proposed. Three schemes are designed to balance privacy protection in centralised datasets and mining accuracy. Some metrics are used to evaluate the performance of the proposed new strategies. Data utility of the three proposed schemes is examined by a binary classification based on the support vector machine. Furthermore, we examine three sparsification strategies. The effect of method parameters on data distortion level and utility is also studied experimentally. Our experimental results on synthetic and real datasets indicate that, in comparison with standard data distortion techniques, the proposed schemes are efficient in balancing data distortion level and data utility. They afford a feasible solution with a good promise for mining accuracy and a significant reduction in the computational cost from SVD.