Reconstruction-based Classification Rule Hiding through Controlled Data Modification

  • Aliki Katsarou
  • Gkoulalas-Divanis Aris
  • Vassilios S. Verykios
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)


In this paper, we propose a reconstruction—based approach to classification rule hiding in categorical datasets. The proposed methodology modifies transactions supporting both sensitive and nonsensitive classification rules in the original dataset and then uses the supporting transactions of the nonsensitive rules to produce its sanitized counterpart. To further investigate some interesting properties of this methodology, we explore three variations of the main technique which differ in the way they select and sanitize transactions supporting sensitive rules. Finally, through extensive experimental evaluation, we demonstrate the effectiveness of the proposed algorithms towards effectively shielding the sensitive knowledge.


Original Dataset Privacy Preserve Categorical Dataset Extensive Experimental Evaluation Sensitive Rule 
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

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Aliki Katsarou
    • 1
  • Gkoulalas-Divanis Aris
    • 2
  • Vassilios S. Verykios
    • 2
  1. 1.Department of ManagementLondon School of Economics and Political ScienceLondonU.K.
  2. 2.Department of Computer & Communication EngineeringUniversity of ThessalyVolosGreece

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