• DocumentCode
    517411
  • Title

    GSSK: A Generalization Step Safe Algorithm in Anonymizing Data

  • Author

    Pin Lv ; Yu Wen-bing ; Chen Nian-sheng

  • Author_Institution
    Sch. of Comput. Sci. & Eng., Wuhan Inst. of Technol., Wuhan, China
  • Volume
    1
  • fYear
    2010
  • fDate
    12-14 April 2010
  • Firstpage
    183
  • Lastpage
    187
  • Abstract
    It is necessary to reduce the steps of generalization in order to minimize information loss in privacy preserving data publishing, but sometimes the anonymous table on basis of the method could still be attacked. To solve the problem, the condition of attack is analyzed, and a m-threshold model is presented to decide whether the value of quasi-identifier attribute would be continuously generalized, making use of algorithm of SSGK dealing with the model. Computer experiments show that the GSSK algorithm can prevent the attack with little information loss.
  • Keywords
    data handling; data privacy; publishing; data anonymity; generalization step safe algorithm; information loss minimization; m-threshold model; privacy preserving data publishing; quasiidentifier attribute; Computer science; Data privacy; Databases; Educational institutions; Intelligent robots; Laboratories; Mobile communication; Mobile computing; Protection; Publishing; Data Privacy; Generalization; k-anonymity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications and Mobile Computing (CMC), 2010 International Conference on
  • Conference_Location
    Shenzhen
  • Print_ISBN
    978-1-4244-6327-5
  • Electronic_ISBN
    978-1-4244-6328-2
  • Type

    conf

  • DOI
    10.1109/CMC.2010.191
  • Filename
    5471489