L-diversity

L-Diversity is a privacy-preserving data anonymization model that extends k-anonymity by requiring each equivalence class in a dataset to contain at least *l* well-represented, semantically distinct values for sensitive attributes, thereby mitigating homogeneity and background knowledge attacks.

104

Publications

10.8K

Citations

238

Authors

110

Institutions

Publications per year

2017–2026

33

Authors

238

  1. UP
    Udai Pratap Rao

    Sardar Vallabhbhai National Institute of Technology Surat, India

    3 publications · 99 citations

    4

    h-index

  2. BB
    Brijesh B. Mehta

    Sardar Vallabhbhai National Institute of Technology Surat, India

    3 publications · 99 citations

    4

    h-index

  3. JH
    Jianmin Han

    Zhejiang Normal University, China

    3 publications · 78 citations

    3

    h-index

  4. GC
    Graham Cormode

    AT&T (United States), United States

    3 publications · 167 citations

    3

    h-index

  5. DS
    Divesh Srivastava

    AT&T (United States), United States

    3 publications · 431 citations

    3

    h-index

1–5 of 238

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 138 citations

    2

    h-index

  2. ACM Transactions on Database Systems

    TODS · 2 publications · 111 citations

    2

    h-index

  3. Lexikos

    2 publications · 25 citations

    2

    h-index

  4. 2

    h-index

  5. Knowledge and Information Systems

    2 publications · 255 citations

    2

    h-index

1–5