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Data Distortion Methods and Metrics in a Terrorist Analysis System

  • Shuting Xu
  • Jun Zhang
Part of the Integrated Series In Information Systems book series (ISIS, volume 18)

Preserving privacy 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 preserving privacy in security-related data mining applications, such as in data mining-based terrorist analysis systems. A sparsified Singular Value Decomposition (SVD) method for data distortion is introduced in this chapter. A few metrics to measure the difference between the distorted dataset and the original dataset and the degree of the privacy protection are also explained in detail. The 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.

Keywords

Support Vector Machine Singular Value Decomposition Privacy Protection Data Mining Technique Real World Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Shuting Xu
    • 1
  • Jun Zhang
    • 2
  1. 1.Department of Computer Information SystemsVirginia State UniversityPetersburgUSA
  2. 2.Department of Computer ScienceUniversity of KentuckyLexingtonUSA

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