Anonymization For Text Data

Anonymization for Text Data is a data protection technique that belongs to the class of anonymization methods and specifically involves the systematic removal, replacement, or obfuscation of personally identifiable information and other sensitive attributes in unstructured textual content to prevent re-identification while preserving the utility of the original text for analysis.

44

Publications

1.6K

Citations

131

Authors

83

Institutions

Publications per year

2017–2026

24

Authors

131

  1. DS
    David Sánchez

    Universitat Rovira i Virgili, Spain

    5 publications · 207 citations

    5

    h-index

  2. MB
    Montserrat Batet

    Universitat Rovira i Virgili, Spain

    3 publications · 116 citations

    3

    h-index

  3. GB
    Ghazaleh Beigi

    Arizona State University, United States

    3 publications · 89 citations

    3

    h-index

  4. HL
    Huan Liu

    Arizona State University, United States

    3 publications · 89 citations

    3

    h-index

  5. FK
    Florian Kerschbaum

    University of Waterloo, Canada

    3 publications · 67 citations

    3

    h-index

1–5 of 131

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 82 citations

    4

    h-index

  2. medRxiv

    Repository · 1 publications · 22 citations

    1

    h-index

  3. Data Mining and Knowledge Discovery

    1 publications · 20 citations

    1

    h-index

  4. Journal of Educational Measurement

    1 publications · 17 citations

    1

    h-index

  5. Technological Forecasting and Social Change

    1 publications · 18 citations

    1

    h-index

1–5