Evaluating the Privacy Implications of Frequent Itemset Disclosure

  • Edoardo Serra
  • Jaideep Vaidya
  • Haritha Akella
  • Ashish Sharma
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 502)

Abstract

Frequent itemset mining is a fundamental data analytics task. In many cases, due to privacy concerns, only the frequent itemsets are released instead of the underlying data. However, it is not clear how to evaluate the privacy implications of the disclosure of the frequent itemsets. Towards this, in this paper, we define the k-distant-IFM-solutions problem, which aims to find k transaction datasets whose pair distance is maximized. The degree of difference between the reconstructed datasets provides a way to evaluate the privacy risk. Since the problem is NP-hard, we propose a 2-approximate solution as well as faster heuristics, and evaluate them on real data.

Keywords

Inverse Frequent itemset Mining Column generation 

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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Edoardo Serra
    • 1
  • Jaideep Vaidya
    • 2
  • Haritha Akella
    • 1
  • Ashish Sharma
    • 1
  1. 1.CS DepartmentBoise State UniversityBoiseUSA
  2. 2.MSIS DepartmentRutgers UniversityNew BrunswickUSA

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