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
226
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
Membership Inference Attacks From First Principles
Nicholas Carlini, Steve Chien, Milad Nasr et al. · 2022 IEEE Symposium on Security and Privacy (SP) · 2022 · 393 citations
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236
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
9 h-index
14 publications
1.5K citations
University of Virginia
Charlottesville, United States
9 h-index
17 publications
851 citations

Huazhong University of Science and Technology
Wuhan, China
8 h-index
28 publications
1.4K citations
Duke University
Durham, United States
7 h-index
11 publications
1.1K citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
12 publications
2.4K citations
The Alan Turing Institute
London, United Kingdom
6 h-index
12 publications
655 citations
1–8 of 236
Repository · 64 publications · 2K citations
26
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TDSC · 8 publications · 349 citations
8
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PoPETs · 7 publications · 696 citations
7
h-index
5 publications · 289 citations
5
h-index
Conference · 4 publications · 158 citations
4
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1–5