• DocumentCode
    2030342
  • Title

    Full-scale privacy preserving for association rule mining

  • Author

    Ma, Tinghuai ; Leng, Jiazhao ; Li, Keyi

  • Author_Institution
    Sch. of Comput. & Software, Nanjing Univ. of Inf. Sci. & Technol., Nanjing, China
  • Volume
    4
  • fYear
    2010
  • fDate
    10-12 Aug. 2010
  • Firstpage
    1668
  • Lastpage
    1672
  • Abstract
    Privacy has become an important issue in Data Mining. Many methods have been brought out to solve this problem. This paper deals with the problem of association rule mining which preserve the confidentiality of each database. In order to find the association rule, each participant has to share their own data. Thus, much privacy information may be broadcasted or been illegal used. These issues can be divided into three categories: data hiding, knowledge hiding and data mining results publishing. This paper reviews the major method of privacy preserving on each category and choose some of them to complete our system. At the end, an improvement of sensitive rules hiding is proposed to make it more accuracy and security.
  • Keywords
    data encapsulation; data mining; data privacy; association rule mining; data hiding; data mining results publishing; database confidentiality; full-scale privacy preserving; knowledge hiding; Association rules; Itemsets; Privacy; Publishing; Security; association rule; data hinding; knowledge hiding; privacy preserving; results publishing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-5931-5
  • Type

    conf

  • DOI
    10.1109/FSKD.2010.5569380
  • Filename
    5569380