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Recent advances in differentially private deep learning have demonstrated that application of differential privacy, specifically the DP-SGD algorithm, has a disparate impact on different sub-groups in the population, which leads to a significantly high drop-in model utility for sub-populations that are under-represented (minorities), compared to well-represented ones.
Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D · 2009
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Reading digits in natural images with unsupervised feature learning, 2011
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., and Ristenpart, T · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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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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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., and Shmatikov, V · 2016
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On fairness, diversity and randomness in algorithmic decision making
Grgić-Hlača, N., Zafar, M. B., Gummadi, K. P., and Weller, A · 2017
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Semi-supervised knowledge transfer for deep learning from private training data
Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I., and Talwar, K · 2017
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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Cited alongside, same era.
The us census bureau adopts differential privacy
Abowd, J. M · 2018
Cited alongside, same era.
Differentially private fair learning
Jagielski, M., Kearns, M., Mao, J., Oprea, A., Roth, A., Sharifi-Malvajerdi, S., and Ullman, J · 2018
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Bagdasaryan, E., Poursaeed, O., and Shmatikov, V · 2019
Cited alongside, same era.
Deep learning with gaussian differential privacy
Bu, Z., Dong, J., Long, Q., and Su, W. J · 2019
Cited alongside, same era.
Neither private nor fair: Impact of data imbalance on utility and fairness in differential privacy
Farrand, T., Mireshghallah, F., Singh, S., and Trask, A · 2020
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Revisiting membership inference under realistic assumptions
Jayaraman, B., Wang, L., Knipmeyer, K., Gu, Q., and Evans, D · 2020
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Privacy in deep learning: A survey
Mireshghallah, F., Taram, M., Vepakomma, P., Singh, A., Raskar, R., and Esmaeilzadeh, H · 2020
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Benchmarking differentially private residual networks for medical imagery
Singh, S., Sikka, H., Kotti, S., and Trask, A · 2020
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Differentially private and fair deep learning: A lagrangian dual approach
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Jayaraman, B. and Evans, D · 2019
Cited alongside, same era.
Fair decision making using privacy-protected data
Kuppam, S., McKenna, R., Pujol, D., Hay, M., Machanavajjhala, A., and Miklau, G · 2019
Cited alongside, same era.
A survey on bias and fairness in machine learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A · 2019
Cited alongside, same era.
Our data, ourselves: Privacy via distributed noise generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., and Naor, M
Cited in the paper.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A
Cited in the paper.
Decision making with differential privacy under a fairness lens
Fioretto, F., Tran, C. D., and Hentenryck, P. V
Cited in the paper.
Differential privacy of hierarchical census data: An optimization approach
Fioretto, F., Van Hentenryck, P., and Zhu, K
Cited in the paper.
Tran, C., Fioretto, F., and Van Hentenryck, P · 2020
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Practical and private (deep) learning without sampling or shuffling
Kairouz, P., McMahan, B., Song, S., Thakkar, O., Thakurta, A., and Xu, Z · 2021
Closest in time.
Chasing your long tails: Differentially private prediction in health care settings
Suriyakumar, V. M., Papernot, N., Goldenberg, A., and Ghassemi, M · 2021
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