Data Distortion for Privacy Protection in a Terrorist Analysis System (original) (raw)

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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Authors and Affiliations

  1. Department of Computer Science, University of Kentucky, Lexington, KY, 40506-0046, USA
    Shuting Xu, Jun Zhang, Dianwei Han & Jie Wang

Authors

  1. Shuting Xu
  2. Jun Zhang
  3. Dianwei Han
  4. Jie Wang

Editor information

Editors and Affiliations

  1. Department of Library and Information Science, Rutgers University,
    Paul Kantor
  2. School of Communication, Information and Library Studies, Rutgers University, 4 Huntington Street, 08901-1071, New Brunswick, NJ, USA
    Gheorghe Muresan
  3. Artificial Solutions, Altonaer Poststraße 13b, 22767, Hamburg, Germany
    Fred Roberts
  4. MIS Department, University of Arizona, 85721, Tucson, AZ, USA
    Daniel D. Zeng
  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China
    Fei-Yue Wang
  6. Department of Management Information Systems, Eller College of Management, The University of Arizona, 85721, AZ, USA
    Hsinchun Chen
  7. College of Computing, Georgia Tech Information Security Center, Georgia Institute of Technology, 801 Atlantic Drive, 30332-0280, Atlanta, GA, USA
    Ralph C. Merkle

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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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