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A Privacy-Preserving Classification Method Based on Singular Value Decomposition

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Journal from gdlhub / 2017-08-14 11:52:32
Oleh : Guang Li, Yadong Wan, IAJIT
Dibuat : 2012-06-23, dengan 1 file

Keyword : Privacy preservation, data mining, singular value decomposition, and sample selection.
Subjek : A Privacy-Preserving Classification Method Based on Singular Value Decomposition
Url : http://www.ccis2k.org/iajit/PDF/vol.9,no.6/2990-5.pdf
Sumber pengambilan dokumen : Internet

With the development of data mining technologies, privacy protection has become a challenge for data mining


applications in many fields. To solve this problem, many privacy-preserving data mining methods have been proposed. One


important type of such methods is based on Singular Value Decomposition (SVD). The SVD-based method provides perturbed


data instead of original data, and users extract original data patterns from perturbed data. The original SVD-based method


perturbs all samples to the same degree. However, in reality, different users have different requirements for privacy protection,


and different samples are not equally important for data mining. Thus, it is better to perturb different samples to different


degrees. This paper improves the SVD-based data perturbation method so that it can perturb different samples to different


degrees. In addition, we propose a new privacy-preserving classification mining method using our improved SVD-based


perturbation method and sample selection. The experimental results indicate that compared with the original SVD-based


method, this new proposed method is more efficient in balancing data privacy and data utility.

Deskripsi Alternatif :

With the development of data mining technologies, privacy protection has become a challenge for data mining


applications in many fields. To solve this problem, many privacy-preserving data mining methods have been proposed. One


important type of such methods is based on Singular Value Decomposition (SVD). The SVD-based method provides perturbed


data instead of original data, and users extract original data patterns from perturbed data. The original SVD-based method


perturbs all samples to the same degree. However, in reality, different users have different requirements for privacy protection,


and different samples are not equally important for data mining. Thus, it is better to perturb different samples to different


degrees. This paper improves the SVD-based data perturbation method so that it can perturb different samples to different


degrees. In addition, we propose a new privacy-preserving classification mining method using our improved SVD-based


perturbation method and sample selection. The experimental results indicate that compared with the original SVD-based


method, this new proposed method is more efficient in balancing data privacy and data utility.

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