• DocumentCode
    178615
  • Title

    Quality Evaluation of an Anonymized Dataset

  • Author

    Fletcher, S. ; Islam, M.Z.

  • Author_Institution
    Sch. of Comput. & Math., Charles Sturt Univ., Bathurst, NSW, Australia
  • fYear
    2014
  • fDate
    24-28 Aug. 2014
  • Firstpage
    3594
  • Lastpage
    3599
  • Abstract
    In this study we argue that the traditional approach of evaluating the information quality of an anonymized (or otherwise modified) dataset is questionable. We propose a novel and simple approach to evaluate the information quality of a modified dataset, and thereby the quality of techniques that modify data. We carry out experiments on eleven datasets and the empirical results strongly support our arguments. We also present some supplementary measures to our approach that provide additional insight into the information quality of modified data.
  • Keywords
    data mining; pattern classification; anonymized dataset; data mining; empirical analysis; information quality evaluation; modified dataset; Accuracy; Cancer; Data privacy; Decision trees; Muscles; Noise; Testing; anonymization; data mining; data quality; information quality; noise addition; privacy preserving data mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (ICPR), 2014 22nd International Conference on
  • Conference_Location
    Stockholm
  • ISSN
    1051-4651
  • Type

    conf

  • DOI
    10.1109/ICPR.2014.618
  • Filename
    6977330