Model Inversion Attacks

Model Inversion Attack is a privacy attack in federated learning where an adversary reconstructs sensitive input data from a machine learning model's parameters or outputs by exploiting the model's inference behavior.

129

Publications

14.8K

Citations

412

Authors

178

Institutions

Publications per year

2017–2026

123

Variants

Model Inversion Attack

Authors

412

  1. SJ
    Somesh Jha

    University of Wisconsin–Madison, United States

    5 publications · 3.5K citations

    5

    h-index

  2. RJ
    Ruoxi Jia

    University of California, Berkeley, United States

    5 publications · 585 citations

    5

    h-index

  3. MF
    Matt Fredrikson

    Carnegie Mellon University, United States

    5 publications · 3.5K citations

    5

    h-index

  4. WZ
    Wanlei Zhou

    Deakin University, Australia

    3 publications · 159 citations

    4

    h-index

  5. TZ
    Tianqing Zhu

    University of Technology Sydney, Australia

    3 publications · 159 citations

    4

    h-index

1–5 of 412

Venues

  1. arXiv (Cornell University)

    Repository · 18 publications · 544 citations

    15

    h-index

  2. 7

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 228 citations

    5

    h-index

  4. IEEE Access

    3 publications · 64 citations

    4

    h-index

  5. Computers & Security

    3 publications · 135 citations

    3

    h-index

1–5