A novel data distortion approach via selective SSVD for privacy protection
by Jie Wang, Jun Zhang, Shuting Xu, Weijun Zhong
International Journal of Information and Computer Security (IJICS), Vol. 2, No. 1, 2008

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.

Online publication date: Thu, 24-Jan-2008

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Computer Security (IJICS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com