Privacy-Preserving Publishing Data with Full Functional Dependencies

  • Hui (Wendy) Wang
  • Ruilin Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)


We study the privacy threat by publishing data that contains full functional dependencies (FFDs). We show that the cross-attribute correlations by FFDs can bring potential vulnerability to privacy. Unfortunately, none of the existing anonymization principles can effectively prevent against the FFD-based privacy attack. In this paper, we formalize the FFD-based privacy attack, define the privacy model (d, l)-inference to combat the FFD-based attack, and design robust anonymization algorithm that achieves (d, l)-inference. The efficiency and effectiveness of our approach are demonstrated by the empirical study.


Information Loss Inference Model Sensitive Attribute Privacy Model Privacy Guarantee 
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

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hui (Wendy) Wang
    • 1
  • Ruilin Liu
    • 1
  1. 1.Stevens Institute of TechnologyHobokenUSA

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