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Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art performance on several graph analytical tasks.
Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
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Towards the science of security and privacy in machine learning
N. Papernot, P. McDaniel, A. Sinha, and M. Wellman · 2016
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Stealing machine learning models via prediction apis
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart · 2016
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Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2016
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Membership inference attacks against machine learning models
R. Shokri, S. Marco, S. Congzheng, and S. Vitaly · 2017
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Machine learning with membership privacy using adversarial regularization
M. Nasr, R. Shokri, and A. Houmansadr · 2018
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A. Salem, Y. Zhang, M. Humbert, P. Berrang, M. Fritz, and M. Backes · 2018
Cited alongside, same era.
Graph Attention Networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
Cited alongside, same era.
Stealing hyperparameters in machine learning
B. Wang and N. Z. Gong · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
N. Carlini, C. Liu, Ú. Erlingsson, J. Kos, and D. Song · 2019
Cited alongside, same era.
Evaluating differentially private machine learning in practice
Exploiting unintended feature leakage in collaborative learning
L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov · 2019
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Simplifying graph convolutional networks
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger · 2019
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On inferring training data attributes in machine learning models
B. Z. H. Zhao, H. J. Asghar, R. Bhaskar, and M. A. Kaafar · 2019
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When differential privacy meets graph neural networks
S. Sajadmanesh and D. Gatica-Perez · 2020
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Updates-leak: Data set inference and reconstruction attacks in online learning
A. Salem, A. Bhattacharya, M. Backes, M. Fritz, and Y. Zhang · 2020
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B. Jayaraman and D. Evans · 2019
Cited alongside, same era.
Memguard: Defending against black-box membership inference attacks via adversarial examples
J. Jia, A. Salem, M. Backes, Y. Zhang, and N. Z. Gong · 2019
Cited alongside, same era.
J. Zhou, C. Chen, L. Zheng, X. Zheng, B. Wu, Z. Liu, and L. Wang · 2020
Later among the works it cites.
Node-level membership inference attacks against graph neural networks
X. He, R. Wen, Y. Wu, M. Backes, Y. Shen, and Y. Zhang · 2021
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