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Generative Adversarial Networks (GANs) have made releasing of synthetic images a viable approach to share data without releasing the original dataset.
Distances of probability measures and random variables
Dudley, R. M · 2010
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Data reuse and the open data citation advantage
Piwowar, H. A. and Vision, T. J · 2013
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 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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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Conditional generative adversarial nets
Mirza, M. and Osindero, S · 2014
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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Arjovsky, M., Chintala, S., and Bottou, L · 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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Gan-based synthetic brain mr image generation
Han, C., Hayashi, H., Rundo, L., Araki, R., Shimoda, W., Muramatsu, S., Furukawa, Y., Mauri, G., and Nakayama, H · 2018
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Pate-gan: Generating synthetic data with differential privacy guarantees
Jordon, J., Yoon, J., and van der Schaar, M · 2018
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Generative model: Membership attack, generalization and diversity
Liu, K. S., Li, B., and Gao, J · 2018
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Understanding membership inferences on well-generalized learning models
Long, Y., Bindschaedler, V., Wang, L., Bu, D., Wang, X., Tang, H., Gunter, C. A., and Chen, K · 2018
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Dropout training, data-dependent regularization, and generalization bounds
Mou, W., Zhou, Y., Gao, J., and Wang, L · 2018
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Machine learning with membership privacy using adversarial regularization
Nasr, M., Shokri, R., and Houmansadr, A · 2018
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Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., and Backes, M · 2018
Monte carlo and reconstruction membership inference attacks against generative models
Hilprecht, B., Härterich, M., and Bernau, D · 2019
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Memguard: Defending against black-box membership inference attacks via adversarial examples
Jia, J., Salem, A., Backes, M., Zhang, Y., and Gong, N. Z · 2019
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Membership inference attacks against adversarially robust deep learning models
Song, L., Shokri, R., and Mittal, P · 2019
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Dp-cgan: Differentially private synthetic data and label generation
Torkzadehmahani, R., Kairouz, P., and Paten, B · 2019
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Demystifying membership inference attacks in machine learning as a service
Truex, S., Liu, L., Gursoy, M. E., Yu, L., and Wei, W · 2019
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Generative adversarial network in medical imaging: A review
Yi, X., Walia, E., and Babyn, P · 2019
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Differentially private generative adversarial network
Xie, L., Lin, K., Wang, S., Wang, F., and Zhou, J · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S · 2018
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Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network
Zheng, R., Liu, L., Zhang, S., Zheng, C., Bunyak, F., Xu, R., Li, B., and Sun, M · 2018
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Privacy and synthetic datasets
Bellovin, S. M., Dutta, P. K., and Reitinger, N · 2019
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Gan-leaks: A taxonomy of membership inference attacks against gans
Chen, D., Yu, N., Zhang, Y., and Fritz, M · 2019
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Logan: Membership inference attacks against generative models
Hayes, J., Melis, L., Danezis, G., and De Cristofaro, E · 2019
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A survey of differentially private generative adversarial networks
Fan, L · 2020
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Revisiting membership inference under realistic assumptions
Jayaraman, B., Wang, L., Evans, D., and Gu, Q · 2020
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Membership inference attacks and defenses in supervised learning via generalization gap
Li, J., Li, N., and Ribeiro, B · 2020
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Mace: A flexible framework for membership privacy estimation in generative models
Liu, X., Xu, Y., Mukherjee, S., and Ferres, J. L · 2020
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