Abstract
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. 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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Xu, S., Zhang, J., Han, D., Wang, J. (2005). Data Distortion for Privacy Protection in a Terrorist Analysis System. In: Kantor, P., et al. Intelligence and Security Informatics. ISI 2005. Lecture Notes in Computer Science, vol 3495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427995_43
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DOI: https://doi.org/10.1007/11427995_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25999-2
Online ISBN: 978-3-540-32063-0
eBook Packages: Computer ScienceComputer Science (R0)