T-closeness

T-Closeness is a privacy-preserving data anonymization model that extends l-diversity by requiring that the distribution of sensitive attribute values within any equivalence class is statistically close to the distribution in the overall dataset, differing by no more than a threshold t, thereby mitigating re-identification and attribute disclosure risks.

35

Publications

4.9K

Citations

91

Authors

45

Institutions

Publications per year

2017–2026

11

Authors

91

  1. JD
    Josep Domingo‐Ferrer

    Universitat Rovira i Virgili, Spain

    4 publications · 329 citations

    4

    h-index

  2. JS
    Jordi Soria-Comas

    Universitat Rovira i Virgili, Spain

    3 publications · 137 citations

    3

    h-index

  3. UP
    Udai Pratap Rao

    Sardar Vallabhbhai National Institute of Technology Surat, India

    1 publications · 13 citations

    2

    h-index

  4. RG
    Ruchika Gupta

    Chandigarh University, India

    1 publications · 13 citations

    2

    h-index

  5. AO
    Akihiko Ohsuga

    University of Electro-Communications, Japan

    2 publications · 114 citations

    2

    h-index

1–5 of 91

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 486 citations

    3

    h-index

  2. ACM Transactions on Knowledge Discovery from Data

    TKDD · 1 publications · 84 citations

    1

    h-index

  3. Journal of Medical Internet Research

    1 publications · 17 citations

    1

    h-index

  4. Journal of Computer Science and Technology

    1 publications · 47 citations

    1

    h-index

  5. IEEE Transactions on Dependable and Secure Computing

    TDSC · 1 publications · 91 citations

    1

    h-index

1–5