• DocumentCode
    3260700
  • Title

    A Crypto-Based Approach to Privacy-Preserving Collaborative Data Mining

  • Author

    Zhan, Justin ; Matwin, Stan

  • Author_Institution
    Heinz Sch., Carnegie Mellon Univ., Pittsburg, PA
  • fYear
    2006
  • fDate
    Dec. 2006
  • Firstpage
    546
  • Lastpage
    550
  • Abstract
    To conduct data mining, we often need to collect data from various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties collaboratively conduct data mining without breaching data privacy presents a challenge. In this paper, we propose a formal definition of privacy, develop a solution for privacy-preserving k-nearest neighbor classification which is one of data mining tasks, and show that our solution preserves data privacy according to our definition
  • Keywords
    cryptography; data mining; data privacy; pattern classification; collaborative data mining; crypto based approach; k-nearest neighbor classification; privacy preservation; Collaboration; Cryptography; Data mining; Data privacy; Data security; Delta modulation; Information security; Information technology; Protection; Protocols;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    0-7695-2702-7
  • Type

    conf

  • DOI
    10.1109/ICDMW.2006.3
  • Filename
    4063687