• DocumentCode
    2929666
  • Title

    (K, G)-anonymity model based on grey relational analysis

  • Author

    Zhang Qishan ; Lin Zhensi ; Zheng Qunhua ; Liu Hong

  • Author_Institution
    Sch. of Manage., Fuzhou Univ., Fuzhou, China
  • fYear
    2013
  • fDate
    15-17 Nov. 2013
  • Firstpage
    16
  • Lastpage
    19
  • Abstract
    k-anonymity is an effective method of privacy preserving. However, some traditional k-anonymity models do not capture diversity and dispersibility of sensitive values in each equivalence class, which makes the privacy disclosure of anonymity table occur easily. In this paper, an advanced (k, g)-anonymity model for numerical data is proposed, and a (k, g)-MDAV algorithm is designed to achieve (k, g)-algorithm. Experimental results show that the algorithm can lower the risk of privacy disclosure while maintaining the data availability.
  • Keywords
    data privacy; equivalence classes; grey systems; (k, g)-MDAV algorithm; (k, g)-anonymity model; anonymity table privacy disclosure risk; data availability; equivalence class; grey relational analysis; maximum distance to average vector algorithm; Algorithm design and analysis; Data models; Data privacy; Educational institutions; Numerical models; Privacy; Silicon; (k; Grey relational analysis; g)-anonymity; k-anonymity; microaggregation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Grey Systems and Intelligent Services, 2013 IEEE International Conference on
  • Conference_Location
    Macao
  • ISSN
    2166-9430
  • Print_ISBN
    978-1-4673-5247-5
  • Type

    conf

  • DOI
    10.1109/GSIS.2013.6714730
  • Filename
    6714730