Singular value decomposition based data distortion strategy for privacy protection

Shuting Xu, Jun Zhang, Dianwei Han, Jie Wang

Research output: Contribution to journalArticlepeer-review

79 Scopus citations


Privacy-preserving 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 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 and the degree of the privacy protection. 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)383-397
Number of pages15
JournalKnowledge and Information Systems
Issue number3
StatePublished - Oct 2006


  • Data distortion
  • Data mining
  • Privacy protection
  • Security
  • Singular value decomposition

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence


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