Fixed-parameter tractability of anonymizing data by suppressing entries

  • Patricia A. Evans
  • H. Todd Wareham
  • Rhonda Chaytor
Article
First Online:

Abstract

A popular model for protecting privacy when person-specific data is released is k -anonymity. A dataset is k-anonymous if each record is identical to at least (k−1) other records in the dataset. The basic k-anonymization problem, which minimizes the number of dataset entries that must be suppressed to achieve k-anonymity, is NP-hard and hence not solvable both quickly and optimally in general. We apply parameterized complexity analysis to explore algorithmic options for restricted versions of this problem that occur in practice. We present the first fixed-parameter algorithms for this problem and identify key techniques that can be applied to this and other k-anonymization problems.

Keywords

Privacy Anonymization Parameterized complexity Fixed-parameter tractability Kernelization 
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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Patricia A. Evans
    • 1
  • H. Todd Wareham
    • 2
  • Rhonda Chaytor
    • 3
  1. 1.Faculty of Computer ScienceUniversity of New BrunswickFrederictonCanada
  2. 2.Department of Computer ScienceMemorial UniversitySt. John’sCanada
  3. 3.School of Computing ScienceSimon Fraser UniversityVancouverCanada

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