• DocumentCode
    2085966
  • Title

    A review on privacy preserving data mining

  • Author

    Shanthi, A.S. ; Karthikeyan, Madurakavi

  • Author_Institution
    Tamilnadu Coll. of Eng., Coimbatore, India
  • fYear
    2012
  • fDate
    18-20 Dec. 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The information is rich: but the knowledge is poor. To gain a better knowledge from available information, number of techniques and methods has been developed in the area of data mining so far. On the other hand privacy is one of the most important properties of information that any system should satisfy. The secrecy of the information must be maintained while sharing the information among different un-trusted parties. Thus privacy plays a major role and also an important issue in most of the data mining applications. Best suited Privacy Preserving Data Mining (PPDM) algorithm for different levels of mining which minimizes the information loss and improves accuracy of the mined data are identified based on this survey.
  • Keywords
    data mining; data privacy; information secrecy; information sharing; privacy preserving data mining; Anonymization; Data mining; Perturbed data; Privacy preserving; Randomization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence & Computing Research (ICCIC), 2012 IEEE International Conference on
  • Conference_Location
    Coimbatore
  • Print_ISBN
    978-1-4673-1342-1
  • Type

    conf

  • DOI
    10.1109/ICCIC.2012.6510302
  • Filename
    6510302