Data Anonymization Through Slicing Based on Graph-Based Vertical Partitioning

  • Kushagra Sharma
  • Aditi Jayashankar
  • K Sharmila Banu
  • B. K. Tripathy
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 44)

Abstract

Data anonymization is a technique that uses data distortion to preserve privacy of public data to be published. Several data anonymization techniques and principles have been proposed in the past such as k-anonymity, l-diversity, and slicing. Slicing promises to address the drawbacks of the other two anonymization models. Our proposition is the use of a graph-based vertical partitioning algorithm (GBVP) in the process of Slicing instead of the originally proposed Partition Around Medoids (PAM). We will present several arguments that favor GBVP against PAM as a choice for clustering algorithm.

Keywords

Data anonymization Slicing Attribute partitioning k-medoids Clustering Graph-based vertical partitioning 

References

  1. 1.
    Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10, 557–570 (2002)Google Scholar
  2. 2.
    Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1(1), 3 (2007)Google Scholar
  3. 3.
    Li, T., Li, N., Zhang, J., Molloy, I.: Slicing: a new approach to privacy preserving data publishing. Arxiv: 0909.2290v1, 12 September (2009)
  4. 4.
    Shamkant, M.R., Navathe, B.: Vertical partitioning for database design. In: ACM, p. 11 (1989)Google Scholar
  5. 5.
    Kaufman, L., Rousueeuw, P.: Finding Groups in Data: an Introduction to Cluster Analysis. Wiley (1990)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Kushagra Sharma
    • 1
  • Aditi Jayashankar
    • 1
  • K Sharmila Banu
    • 1
  • B. K. Tripathy
    • 1
  1. 1.School of Computing Science & EngineeringVIT UniversityVelloreIndia

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