Two-Phase Iterative Algorithm

  • Aris Gkoulalas-Divanis
  • Vassilios S. Verykios
Part of the Advances in Database Systems book series (ADBS, volume 41)


In this chapter, we present a two–phase iterative algorithm (proposed in [27]) that extends the functionality of the inline algorithm of [23] (Chapter 14) to allow for the identification of exact hiding solutions for a wider spectrum of problem instances. A problem instance is defined as the set of (i) the original dataset \(\mathcal{D_O}\), (ii) the minimum frequency threshold mfreq that is considered for its mining, and (iii) the set of sensitive itemsets S that have to be protected. Since the inline algorithm allows only supported items in \(\mathcal{D_O}\) to become unsupported in \(\mathcal{D}\), there exist problem instances that although they allow for an exact hiding solution, the inline approach is incapable of finding it. The truthfulness of this statement can be observed in the experiments provided in Section 15.4, as well as in the experimental evaluation of [26].


Problem Instance Frequent Itemset Feasible Constraint Minimum Support Threshold Downward Closure Property 
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 Science+Business Media, LLC 2010

Authors and Affiliations

  • Aris Gkoulalas-Divanis
    • 1
  • Vassilios S. Verykios
    • 2
  1. 1.Information Analytics LabIBM Research GmbH - ZurichRueschlikonSwitzerland
  2. 2.Department of Computer and Communication EngineeringUniversity of ThessalyVolosGreece

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