Sensitive Data Anonymization

Sensitive Data Anonymization is a methodological process within open data policy that systematically removes or obfuscates personally identifiable and confidential information from datasets to prevent re-identification, thereby enabling data sharing while preserving individual privacy.

450

Publications

45.1K

Citations

1.1K

Authors

467

Institutions

Publications per year

2017–2026

167

Authors

1.1K

  1. BC
    Benjamin C. M. Fung

    McGill University, Canada

    12 publications · 1.3K citations

    12

    h-index

  2. YT
    Yufei Tao

    Chinese University of Hong Kong, Hong Kong SAR China

    8 publications · 1.5K citations

    8

    h-index

  3. XX
    Xiaokui Xiao

    Nanyang Technological University, Singapore

    8 publications · 1.4K citations

    8

    h-index

  4. LX
    Li Xiong

    Emory University, United States

    7 publications · 215 citations

    7

    h-index

  5. DS
    David Sánchez

    Universitat Rovira i Virgili, Spain

    7 publications · 349 citations

    7

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 328 citations

    12

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 10 publications · 728 citations

    10

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 10 publications · 884 citations

    10

    h-index

  4. SSRN Electronic Journal

    Repository · 7 publications · 1.2K citations

    7

    h-index

  5. Knowledge-Based Systems

    6 publications · 283 citations

    6

    h-index

1–5