Membership Inference

Membership Inference is a type of privacy attack in which an adversary determines whether a specific data record was part of the training dataset of a machine learning model, thereby revealing sensitive information about individuals' participation in the data.

226

Publications

17.4K

Citations

644

Authors

236

Institutions

Publications per year

2017–2026

216

Authors

644

  1. MB
    Michael Backes

    Saarland University, Germany

    13 publications · 947 citations

    13

    h-index

  2. RS
    Reza Shokri

    National University of Singapore, Singapore

    13 publications · 5.2K citations

    12

    h-index

  3. ED
    Emiliano De Cristofaro

    University College London, United Kingdom

    13 publications · 963 citations

    12

    h-index

  4. MF
    Mario Fritz

    Max Planck Institute for Informatics, Germany

    8 publications · 553 citations

    8

    h-index

  5. CT
    Carmela Troncoso

    École Polytechnique Fédérale de Lausanne, Switzerland

    7 publications · 119 citations

    7

    h-index

1–5 of 644

Venues

  1. arXiv (Cornell University)

    Repository · 64 publications · 2K citations

    26

    h-index

  2. IEEE Transactions on Dependable and Secure Computing

    TDSC · 8 publications · 349 citations

    8

    h-index

  3. Proceedings on Privacy Enhancing Technologies

    PoPETs · 7 publications · 696 citations

    7

    h-index

  4. 5

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 158 citations

    4

    h-index

1–5