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Given the increase in the use of personal data for training Deep Neural Networks (DNNs) in tasks such as medical imaging and diagnosis, differentially private training of DNNs is surging in importance and there is a large body of work focusing on providing better privacy-utility trade-off.
Advancements in image classification using convolutional neural network
Sultana, F., Sufian, A., and Dutta, P · 1905
Earlier work this paper cites.
Benchmarking differentially private residual networks for medical imagery
Singh, S. and Sikka, H · 2005
Earlier work this paper cites.
Model explanations with differential privacy
Patel, N., Shokri, R., and Zick, Y · 2006
Earlier work this paper cites.
Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D · 2011
Earlier work this paper cites.
Differentially private learning needs better features (or much more data)
Tramèr, F. and Boneh, D · 2011
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Dwork, C. and Roth, A · 2014
Earlier work this paper cites.
Learning deep features for discriminative localization, 2015
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Cited alongside, same era.
Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., and Batra, D · 2016
Cited alongside, same era.
Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising, 2018
Balle, B. and Wang, Y.-X · 2018
Cited alongside, same era.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V. N · 2018
Cited alongside, same era.
Recent progress in semantic image segmentation
Liu, X., Deng, Z., and Yang, Y · 2018
Cited alongside, same era.
Image captioning with attribute refinement
Huang, Y., Li, C., Li, T., Wan, W., and Chen, J · 2019
Later among the works it cites.
Privacy in deep learning: A survey
Mirshghallah, F., Taram, M., Vepakomma, P., Singh, A., Raskar, R., and Esmaeilzadeh, H · 2020
Later among the works it cites.
End-to-end privacy preserving deep learning on multi-institutional medical imaging
Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M.-M., et al · 2021
Closest in time.
U-noise: Learnable noise masks for interpretable image segmentation
Koker, T., Mireshghallah, F., Titcombe, T., and Kaissis, G · 2021
Closest in time.
Fedpandemic: A cross-device federated learning approach towards elementary prognosis of diseases during a pandemic, 2021
Priyanshu, A. and Naidu, R · 2021
Closest in time.
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URL https://www.kaggle.com/c/aptos2019-blindness-detection/data
Aptos 2019 diabetic retinopathy dataset · 2019
Cited alongside, same era.
Local differential privacy: a tutorial
Bebensee, B · 2019
Cited alongside, same era.
Chasing your long tails: Differentially private prediction in health care settings
Suriyakumar, V. M., Papernot, N., Goldenberg, A., and Ghassemi, M · 2021
Closest in time.