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
223
Membership Inference Attacks Against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song et al. · 2017 · 4.2K citations
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson et al. · 2018 · 904 citations
LOGAN: Membership Inference Attacks Against Generative Models
Jamie Hayes, Luca Melis, George Danezis et al. · Proceedings on Privacy Enhancing Technologies · 2018 · 420 citations · Full text
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225
Helmholtz Center for Information Security
Saarbrücken, Germany
16 h-index
51 publications
5.1K citations
University College London
London, United Kingdom
12 h-index
29 publications
2.9K citations
National University of Singapore
Singapore, Singapore
10 h-index
15 publications
3.1K citations
University of Virginia
Charlottesville, United States
8 h-index
16 publications
810 citations

Huazhong University of Science and Technology
Wuhan, China
8 h-index
28 publications
1.4K citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
12 publications
2.4K citations

University of Massachusetts Amherst
Amherst Center, United States
6 h-index
10 publications
4.6K citations
The Alan Turing Institute
London, United Kingdom
6 h-index
12 publications
655 citations
1–8 of 225
Repository · 67 publications · 2.3K citations
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TDSC · 7 publications · 298 citations
7
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PoPETs · 7 publications · 696 citations
7
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6 publications · 359 citations
6
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3 publications · 298 citations
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