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Recent works have demonstrated that machine learning models are vulnerable to model inversion attacks, which lead to the exposure of sensitive information contained in their training dataset.
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I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, “Generative adversarial nets", Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 2672–2680, 2014
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M. Abadi, U. Erlingsson, I. Goodfellow, H.B. McMahan, I. Mironov, N. Papernot, K. Talwar and L. Zhang, “On the protection of private information in machine learning systems:Two recent apparoches”, Proceedings of the 30th Computer Security Foundations Symposium (CSF), pp. 1–6, 2017
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M.A. Rahman, T. Rahman and R. Laganière, N. Mohammed and Y. Wang, “Membership inference attack against differentially private deep learning model", Transactions On Data Privacy 11(1): 61–79, 2018
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2016
Cited alongside, same era.
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter and T. Ristenpart, “Stealing machine learning models via prediction APIs”, Proceedings of 25th USENIX Security Symposium , pp. 601–618, 2016
2016
Cited alongside, same era.
X. Wu, M. Fredrikson, S. Jha and J.F. Naughton, “A methodology for formalizing model-inversion attacks”, Proceedings of the IEEE Computer Security Foundations Symposium (CSF), pp. 355–370, 2016
2016
Cited alongside, same era.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford and X. Chen., “Improved techniques for training gans”, Proceedings of Neural Information Processing Systems (NIPS), pp. 2226–2234, 2016
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M. Abadi, A. Chu, I. Goodfellow, H.B. McMahan, I. Mironov, K. Talwar and L Zhang, "Deep learning with differential privacy", Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 308–318, 2016
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N. Papernot, P. Mcdaniel, A. Swami and R. Harang, “Crafting adversarial input sequences for recurrent neural networks”, Proceedings of the IEEE Military Communications Conference (MILCOM), pp. 7–12, 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
R. Shokri, M. Stronati, C. Song and V. Shmatikov, “Membership inference attacks against machine learning models”, Proceedings of IEEE Symposium on Security and Privacy (S&P), pp. 3–18, 2017
2017
Cited alongside, same era.
Later among the works it cites.
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh and P. McDaniel, “Ensemble adversarial training: Attacks and defenses”, Proceedings of the International Conference on Learning Representations (ICLR), 2018
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2018
Later among the works it cites.
J. Hayes, L. Melis, G. Danezis and E. De Cristofaro, “Logan: Membership inference attacks against generative models”, Proceedings on Privacy Enhancing Technologies (PoPETs) 1: 133–152, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Hidano, T. Murakami, S. Katsumata, S. Kiyomoto, and G. Hanaoka, “Model inversion attacks for online prediction systems: without knowledge of non-sensitive attributes”, IEICE Transactions on Information and Systems 101(11):2665–2676, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
L. Melis, C. Song, E. De Cristofaro and V. Shmatikov, “Exploiting unintended feature leakage in collaborative learning”, Proceedings of 2019 IEEE Symposium on Security & Privacy (S&P), 2019
2019
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2019
Closest in time.
2019
Closest in time.
B. Jayaraman and D. Evans, “Evaluating differentially private machine learning in practice”, Proceedings of the 28th USENIX Security Symposium , 2019
2019
Closest in time.