K-anonymity

K-Anonymity is a privacy-preserving data anonymization model that ensures each individual in a dataset cannot be distinguished from at least k−1 other individuals with respect to their quasi-identifying attributes.

586

Publications

60.2K

Citations

1.4K

Authors

556

Institutions

Publications per year

2017–2026

177

Authors

1.4K

  1. VT
    Vicenç Torra

    Artificial Intelligence Research Institute, Spain

    13 publications · 931 citations

    13

    h-index

  2. JD
    Josep Domingo‐Ferrer

    Universitat Rovira i Virgili, Spain

    13 publications · 1K citations

    13

    h-index

  3. HW
    Hua Wang

    Victoria University, Australia

    13 publications · 521 citations

    12

    h-index

  4. JL
    Jiuyong Li

    University of South Australia, Australia

    11 publications · 954 citations

    11

    h-index

  5. CC
    Chris Clifton

    Purdue University West Lafayette, United States

    10 publications · 1.4K citations

    10

    h-index

1–5 of 1.4K

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 24 publications · 4.5K citations

    21

    h-index

  2. IEEE Access

    19 publications · 631 citations

    14

    h-index

  3. 6

    h-index

  4. arXiv (Cornell University)

    Repository · 5 publications · 123 citations

    5

    h-index

  5. Procedia Computer Science

    5 publications · 87 citations

    5

    h-index

1–5