Anonymization For Graph Data

Anonymization for Graph Data is a data protection methodology that transforms graph-structured datasets by modifying their topological and attribute features to prevent the re-identification of individuals while preserving utility for analytical tasks.

123

Publications

8.1K

Citations

319

Authors

152

Institutions

Publications per year

2017–2026

42

Authors

319

  1. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    9 publications · 442 citations

    9

    h-index

  2. SJ
    Shouling Ji

    Zhejiang University, China

    5 publications · 312 citations

    5

    h-index

  3. JC
    Jordi Casas-Roma

    Universitat Oberta de Catalunya, Spain

    5 publications · 172 citations

    5

    h-index

  4. RB
    Raheem Beyah

    Georgia Institute of Technology, United States

    5 publications · 312 citations

    5

    h-index

  5. FL
    Feng Li

    Indiana University – Purdue University Indianapolis, United States

    5 publications · 112 citations

    5

    h-index

1–5 of 319

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 5 publications · 142 citations

    5

    h-index

  2. IEEE Access

    5 publications · 367 citations

    5

    h-index

  3. arXiv (Cornell University)

    Repository · 4 publications · 82 citations

    4

    h-index

  4. Knowledge and Information Systems

    4 publications · 129 citations

    4

    h-index

  5. Social Network Analysis and Mining

    3 publications · 91 citations

    3

    h-index

1–5