De-anonymization

De-Anonymization is a privacy threat modeling method that distinguishes itself by re-identifying individuals from anonymized data through the linkage of residual identifiers or auxiliary information.

377

Publications

22.8K

Citations

1.1K

Authors

451

Institutions

Publications per year

2017–2026

164

Variants

Re-identification

Authors

1.1K

  1. SJ
    Shouling Ji

    Zhejiang University, China

    11 publications · 595 citations

    11

    h-index

  2. RB
    Raheem Beyah

    Georgia Institute of Technology, United States

    10 publications · 580 citations

    10

    h-index

  3. WL
    Weiqing Li

    Nanjing University of Science and Technology, China

    8 publications · 424 citations

    8

    h-index

  4. AN
    Arvind Narayanan

    Princeton University, United States

    8 publications · 4.4K citations

    8

    h-index

  5. DS
    David Sánchez

    Universitat Rovira i Virgili, Spain

    8 publications · 348 citations

    8

    h-index

1–5 of 1.1K

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 14 publications · 677 citations

    13

    h-index

  2. arXiv (Cornell University)

    Repository · 13 publications · 421 citations

    12

    h-index

  3. IEEE Access

    10 publications · 246 citations

    9

    h-index

  4. IEEE Transactions on Dependable and Secure Computing

    TDSC · 8 publications · 423 citations

    8

    h-index

  5. Procedia Computer Science

    5 publications · 88 citations

    5

    h-index

1–5