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The Effectiveness of Data Shuffling for Privacy-Preserving Data Mining Applications

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  • Han Li
  • Krishnamurty Muralidhar
  • Rathindra Sarathy

Abstract

Preserving the confidentiality of sensitive data, while permitting knowledge discovery, is an important goal in privacy-preserving data mining. This paper investigates the effectiveness of data shuffling for classification tree and regression analysis. We compare the effectiveness of data shuffling to the tree based data perturbation method which was developed specifically for the purpose of data mining. Results suggest that data shuffling provides the higher levels of data security and more effectively preserves data mining knowledge than tree based data perturbation method.

Suggested Citation

  • Han Li & Krishnamurty Muralidhar & Rathindra Sarathy, 2012. "The Effectiveness of Data Shuffling for Privacy-Preserving Data Mining Applications," Journal of Information Privacy and Security, Taylor & Francis Journals, vol. 8(2), pages 3-17, April.
    • Handle: RePEc:taf:uipsxx:v:8:y:2012:i:2:p:3-17
    DOI: 10.1080/15536548.2012.10845652
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