Data distortion for privacy protection in a terrorist analysis system

Shuting Xu, Jun Zhang, Dianwei Han, Jie Wang

Research output: Contribution to journalConference articlepeer-review

28 Scopus citations


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.

Original languageEnglish
Pages (from-to)459-464
Number of pages6
JournalLecture Notes in Computer Science
StatePublished - 2005
EventIEEE International Conference on Intelligence and Security Informatics, ISI 2005 - Atlanta, GA, United States
Duration: May 19 2005May 20 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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