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Machine learning models' goal is to make correct predictions for specific tasks by learning important properties and patterns from data.
Ultimate power of inference attacks: Privacy risks of learning high-dimensional graphical models
Murakonda, S. K., Shokri, R., and Theodorakopoulos, G. (2019) · 1905
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Quantification of the leakage in federated learning
Li, Z., Huang, Z., Chen, C., and Hong, C. (2019) · 1910
Earlier work this paper cites.
Eavesdrop the composition proportion of training labels in federated learning
Wang, L., Xu, S., Wang, X., and Zhu, Q. (2019a) · 1910
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Towards privacy and security of deep learning systems: a survey
He, Y., Meng, G., Chen, K., Hu, X., and He, J. (2019) · 1911
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Inverting gradients–how easy is it to break privacy in federated learning?
Geiping, J., Bauermeister, H., Dröge, H., and Moeller, M. (2020) · 2003
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Sponge examples: Energy-latency attacks on neural networks
Shumailov, I., Zhao, Y., Bates, D., Papernot, N., Mullins, R., and Anderson, R. (2020) · 2006
Earlier work this paper cites.
The good, the bad, and the ugly: Quality inference in federated learning
Pejó, B. (2020) · 2007
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A survey of privacy attacks in machine learning
Rigaki, M. and Garcia, S. (2020) · 2007
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The effects of artificial gender imbalance: Science & society series on sex and science
Hesketh, T. and Min, J. M. (2012) · 2012
Earlier work this paper cites.
Black-box model inversion attribute inference attacks on classification models
Mehnaz, S., Li, N., and Bertino, E. (2020) · 2012
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2013) · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2014) · 2014
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Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
Ateniese, G., Mancini, L. V., Spognardi, A., Villani, A., Vitali, D., and Felici, G. (2015) · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T. (2015) · 2015
Cited alongside, same era.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
Cited alongside, same era.
Using machine teaching to identify optimal training-set attacks on machine learners
Mei, S. and Zhu, X. (2015) · 2015
Cited alongside, same era.
Privacy-preserving deep learning
Shokri, R. and Shmatikov, V. (2015) · 2015
Cited alongside, same era.
Practical black-box attacks against deep learning systems using adversarial examples
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., and Swami, A. (2016) · 2016
Cited alongside, same era.
Stealing machine learning models via prediction apis
Algorithms that remember: model inversion attacks and data protection law
Veale, M., Binns, R., and Edwards, L. (2018) · 2018
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Stealing hyperparameters in machine learning
Wang, B. and Gong, N. Z. (2018) · 2018
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Exploiting unintended feature leakage in collaborative learning
Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V. (2019) · 2019
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Beyond inferring class representatives: User-level privacy leakage from federated learning
Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., and Qi, H. (2019b) · 2019
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To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the covid-19 pandemic
Eikenberry, S. E., Mancuso, M., Iboi, E., Phan, T., Eikenberry, K., Kuang, Y., Kostelich, E., and Gumel, A. B. (2020) · 2020
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Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., and Ristenpart, T. (2016) · 2016
Cited alongside, same era.
Deep models under the gan: information leakage from collaborative deep learning
Hitaj, B., Ateniese, G., and Perez-Cruz, F. (2017) · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., and Swami, A. (2017) · 2017
Cited alongside, same era.
GDPR and the Concept of Risk
Demetzou, K. (2018) · 2018
Cited alongside, same era.
Property inference attacks on fully connected neural networks using permutation invariant representations
Ganju, K., Wang, Q., Yang, W., Gunter, C. A., and Borisov, N. (2018) · 2018
Cited alongside, same era.
Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., and Li, B. (2018) · 2018
Cited alongside, same era.
Explanation methods in deep learning: Users, values, concerns and challenges
Ras, G., van Gerven, M., and Haselager, P. (2018) · 2018
Cited alongside, same era.
Kim, T. and Yang, J. (2020) · 2020
Later among the works it cites.
Gender obfuscation through face morphing
Wang, S. (2020) · 2020
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The secret revealer: Generative model-inversion attacks against deep neural networks
Zhang, Y., Jia, R., Pei, H., Wang, W., Li, B., and Song, D. (2020) · 2020
Later among the works it cites.
Deep leakage from gradients
Zhu, L. and Han, S. (2020) · 2020
Later among the works it cites.
Declaration on ethics and data protection in artificial intelligence
Commission Nationale de l’Informatique et des Libertés (CNIL), European Data Protection Supervisor (EDPS), and Garante per la protezione dei dati personali (2018) · 2021
Closest in time.
Guidelines on artificial intelligence and data protection
Council of Europe (2019) · 2021
Closest in time.
Artificial intelligence and privacy
The Norwegian Data Protection Authority (Datatilsynet) (2018) · 2021
Closest in time.