Sanitization of Databases for Refined Privacy Trade-Offs

  • Ahmed HajYasien
  • Vladimir Estivill-Castro
  • Rodney Topor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)


In this paper, we propose a new heuristic algorithm called the QIBC algorithm that improves the privacy of sensitive knowledge (as itemsets) by blocking more inference channels. We show that the existing sanitizing algorithms for such task have fundamental drawbacks. We show that previous methods remove more knowledge than necessary for unjustified reasons or heuristically attempt to remove the minimum frequent non-sensitive knowledge but leave open inference channels that lead to discovery of hidden sensitive knowledge. We formalize the refined problem and prove it is NP-hard. Finally, experimental results show the practicality of the new QIBC algorithm.


Association Rule Heuristic Algorithm Frequent Itemsets Mining Association Rule Frequent Itemset Mining 
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 2006

Authors and Affiliations

  • Ahmed HajYasien
    • 1
  • Vladimir Estivill-Castro
    • 1
  • Rodney Topor
    • 1
  1. 1.IIISGriffith UniversityAustralia

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