Membership Inference Attacks

Membership Inference Attack is a type of privacy attack in machine learning wherein an adversary determines whether a specific data sample was part of the training dataset used to build a model, thereby distinguishing the model’s behavior on members versus non-members of its training set.

223

Publications

19.1K

Citations

642

Authors

225

Institutions

Publications per year

2017–2026

220

Authors

642

  1. RS
    Reza Shokri

    National University of Singapore, Singapore

    15 publications · 6.9K citations

    13

    h-index

  2. MB
    Michael Backes

    Saarland University, Germany

    13 publications · 947 citations

    13

    h-index

  3. ED
    Emiliano De Cristofaro

    University College London, United Kingdom

    14 publications · 1K citations

    12

    h-index

  4. MF
    Mario Fritz

    Max Planck Institute for Informatics, Germany

    8 publications · 553 citations

    8

    h-index

  5. MN
    Milad Nasr

    University of Massachusetts Amherst, United States

    7 publications · 2.6K citations

    7

    h-index

1–5 of 642

Venues

  1. arXiv (Cornell University)

    Repository · 67 publications · 2.3K citations

    28

    h-index

  2. IEEE Transactions on Dependable and Secure Computing

    TDSC · 7 publications · 298 citations

    7

    h-index

  3. Proceedings on Privacy Enhancing Technologies

    PoPETs · 7 publications · 696 citations

    7

    h-index

  4. 6

    h-index

  5. IEEE Access

    3 publications · 298 citations

    3

    h-index

1–5