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To prevent unintentional data leakage, research community has resorted to data generators that can produce differentially private data for model training.
An efficient method for finding the minimum of a function of several variables without calculating derivatives
Powell, M. J. D · 1964
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al · 1998
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Signal recovery from pooling representations
Estrach, J. B., Szlam, A., and LeCun, Y · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Conditional generative adversarial nets
Mirza, M. and Osindero, S · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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The composition theorem for differential privacy
Kairouz, P., Oh, S., and Viswanath, P · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Deep neural networks with random gaussian weights: A universal classification strategy?
Giryes, R., Sapiro, G., and Bronstein, A. M · 2016
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Rényi differential privacy
Mironov, I · 2017
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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Hongler, C., and Gabriel, F · 2018
Cited alongside, same era.
Wang, T., Zhu, J., Torralba, A., and Efros, A. A · 2018
Cited alongside, same era.
Differentially private generative adversarial network
Xie, L., Lin, K., Wang, S., Wang, F., and Zhou, J · 2018
Cited alongside, same era.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
Cited alongside, same era.
Label-only membership inference attacks
Choquette-Choo, C. A., Tramer, F., Carlini, N., and Papernot, N · 2021
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Dp-merf: Differentially private mean embeddings with randomfeatures for practical privacy-preserving data generation
Harder, F., Adamczewski, K., and Park, M · 2021
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Membership leakage in label-only exposures
Li, Z. and Zhang, Y · 2021
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Adversary instantiation: Lower bounds for differentially private machine learning
Nasr, M., Songi, S., Thakurta, A., Papemoti, N., and Carlin, N · 2021
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On the difficulty of membership inference attacks
Rezaei, S. and Liu, X · 2021
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Dpgen: Automated program synthesis for differential privacy
Wang, Y., Ding, Z., Xiao, Y., Kifer, D., and Zhang, D · 2021
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Exploiting unintended feature leakage in collaborative learning
Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V · 2019
Cited alongside, same era.
White-box vs black-box: Bayes optimal strategies for membership inference
Sablayrolles, A., Douze, M., Schmid, C., Ollivier, Y., and Jégou, H · 2019
Cited alongside, same era.
Soft-label dataset distillation and text dataset distillation
Sucholutsky, I. and Schonlau, M · 2019
Cited alongside, same era.
Flexible dataset distillation: Learn labels instead of images
Bohdal, O., Yang, Y., and Hospedales, T · 2020
Cited alongside, same era.
Gan-leaks: A taxonomy of membership inference attacks against generative models
Chen, D., Yu, N., Zhang, Y., and Fritz, M · 2020
Cited alongside, same era.
Threats to federated learning
Lyu, L., Yu, H., Zhao, J., and Yang, Q · 2020
Cited alongside, same era.
Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data
Such, F. P., Rawal, A., Lehman, J., Stanley, K. O., and Clune, J · 2020
Cited alongside, same era.
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This person (probably) exists. identity membership attacks against gan generated faces
Webster, R., Rabin, J., Simon, L., and Jurie, F · 2021
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How does data augmentation affect privacy in machine learning?
Yu, D., Zhang, H., Chen, W., Yin, J., and Liu, T.-Y · 2021
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Dataset condensation with gradient matching
Zhao, B., Mopuri, K. R., and Bilen, H · 2021
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Membership inference attacks from first principles
Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., and Tramer, F · 2022
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Dataset distillation by matching training trajectories
Cazenavette, G., Wang, T., Torralba, A., Efros, A. A., and Zhu, J.-Y · 2022
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Graph condensation for graph neural networks
Jin, W., Zhao, L., Zhang, S., Liu, Y., Tang, J., and Shah, N · 2022
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Cafe: Learning to condense dataset by aligning features
Wang, K., Zhao, B., Peng, X., Zhu, Z., Yang, S., Wang, S., Huang, G., Bilen, H., Wang, X., and You, Y · 2022
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