• DocumentCode
    2294382
  • Title

    Extended K-Anonymity Models Against Attribute Disclosure

  • Author

    Sun, Xiaoxun ; Wang, Hua ; Sun, Lili

  • Author_Institution
    Dept. of Math. & Comput., Univ. of Southern Queensland, Toowoomba, QLD, Australia
  • fYear
    2009
  • fDate
    19-21 Oct. 2009
  • Firstpage
    130
  • Lastpage
    136
  • Abstract
    P-sensitive k-anonymity model has been recently defined as a sophistication of k-anonymity. This new property requires that there be at least p distinct values for each sensitive attribute within the records sharing a combination of key attributes. However, as shown in this paper, it may not protect sensitive information in some way. In this paper, we empirically investigate two enhanced k-anonymity models. Instead of publishing original specific sensitive attributes, the new models publish the categories that the sensitive values belong to. We propose a top-down approach to implement two enhanced models and show in the comprehensive experimental evaluations that the two new introduced models are practical in terms of effectiveness and efficiency.
  • Keywords
    security of data; attribute disclosure; extended k-anonymity models; p-sensitive k-anonymity model; sensitive information; specific sensitive attributes; top-down approach; Computer networks; Data security; Information security; Joining processes; Mathematical model; Mathematics; Medical conditions; Protection; Publishing; Sun;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Network and System Security, 2009. NSS '09. Third International Conference on
  • Conference_Location
    Gold Coast, QLD
  • Print_ISBN
    978-1-4244-5087-9
  • Electronic_ISBN
    978-0-7695-3838-9
  • Type

    conf

  • DOI
    10.1109/NSS.2009.23
  • Filename
    5318942