• DocumentCode
    2850240
  • Title

    Privacy-preserving outlier detection

  • Author

    Vaidya, Jaideep ; Clifton, Chris

  • Author_Institution
    Rutgers Univ., Newark, NJ, USA
  • fYear
    2004
  • fDate
    1-4 Nov. 2004
  • Firstpage
    233
  • Lastpage
    240
  • Abstract
    Outlier detection can lead to the discovery of truly unexpected knowledge in many areas such as electronic commerce, credit card fraud and especially national security. We look at the problem of finding outliers in large distributed databases where privacy/security concerns restrict the sharing of data. Both homogeneous and heterogeneous distribution of data is considered. We propose techniques to detect outliers in such scenarios while giving formal guarantees on the amount of information disclosed.
  • Keywords
    data mining; data privacy; distributed databases; security of data; very large databases; data sharing; heterogeneous data distribution; homogeneous data distribution; knowledge discovery; large distributed databases; privacy-preserving outlier detection; Credit cards; Data mining; Data privacy; Data security; Distributed databases; Electronic commerce; Information security; National security; Protocols; Terrorism;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on
  • Print_ISBN
    0-7695-2142-8
  • Type

    conf

  • DOI
    10.1109/ICDM.2004.10081
  • Filename
    1410289