Anonymization For Tabular Data

Anonymization for Tabular Data is a data protection technique that belongs to the class of anonymization methods and specifically involves the systematic transformation of structured, attribute-value formatted datasets to prevent the re-identification of individuals while preserving data utility for analysis.

14

Publications

867

Citations

36

Authors

18

Institutions

Publications per year

2017–2026

3

Authors

36

  1. GC
    Graham Cormode

    AT&T (United States), United States

    2 publications · 273 citations

    2

    h-index

  2. JC
    Jordi Castro

    Universitat Politècnica de Catalunya, Spain

    2 publications · 59 citations

    2

    h-index

  3. DS
    Divesh Srivastava

    AT&T (United States), United States

    2 publications · 273 citations

    2

    h-index

  4. TY
    Ting Yu

    North Carolina State University, United States

    2 publications · 273 citations

    2

    h-index

  5. QZ
    Qing Zhang

    Huazhong University of Science and Technology, China

    2 publications · 273 citations

    2

    h-index

1–5 of 36

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 54 citations

    1

    h-index

  2. INFORMS journal on computing

    1 publications · 26 citations

    1

    h-index

  3. ACM Transactions on Privacy and Security

    1 publications · 14 citations

    1

    h-index

  4. European Journal of Operational Research

    1 publications · 33 citations

    1

    h-index

  5. 1

    h-index

1–5