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Recent works have shown that selecting an optimal model architecture suited to the differential privacy setting is necessary to achieve the best possible utility for a given privacy budget using differentially private stochastic gradient descent (DP-SGD)(Tramer and Boneh 2020; Cheng et al.
Removing disparate impact on model accuracy in differentially private stochastic gradient descent
Xu, D.; Du, W.; and Wu, X. 2021 · 1932
Earlier work this paper cites.
LSAC National Longitudinal Bar Passage Study. LSAC Research Report Series
Wightman, L. F. 1998 · 1998
Earlier work this paper cites.
Removing disparate impact of differentially private stochastic gradient descent on model accuracy
Xu, D.; Du, W.; and Wu, X. 2020 · 2003
Earlier work this paper cites.
Our data, ourselves: Privacy via distributed noise generation
Dwork, C.; Kenthapadi, K.; McSherry, F.; Mironov, I.; and Naor, M. 2006a · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Dwork, C.; McSherry, F.; Nissim, K.; and Smith, A. 2006b · 2006
Earlier work this paper cites.
Differentially private learning needs better features (or much more data)
Tramer, F.; and Boneh, D. 2020 · 2011
Earlier work this paper cites.
Fairness through awareness
Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012 · 2012
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Dwork, C.; Roth, A.; et al. 2014 · 2014
Earlier work this paper cites.
Certifying and removing disparate impact
Feldman, M.; Friedler, S. A.; Moeller, J.; Scheidegger, C.; and Venkatasubramanian, S. 2015 · 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 · 2016
Earlier work this paper cites.
Machine bias
Angwin, J.; Larson, J.; Mattu, S.; and Kirchner, L. 2016 · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
Earlier work this paper cites.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A. 2017 · 2017
Earlier work this paper cites.
”UCI Machine Learning Repository”
Dua, D.; and Graff, C. 2017 · 2017
Earlier work this paper cites.
Rényi differential privacy
Mironov, I. 2017 · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Shokri, R.; Stronati, M.; Song, C.; and Shmatikov, V. 2017 · 2017
Cited alongside, same era.
The frontiers of fairness in machine learning
Chouldechova, A.; and Roth, A. 2018 · 2018
Cited alongside, same era.
Scalable private learning with pate
Papernot, N.; Song, S.; Mironov, I.; Raghunathan, A.; Talwar, K.; and Erlingsson, Ú. 2018 · 2018
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Bagdasaryan, E.; Poursaeed, O.; and Shmatikov, V. 2019 · 2019
Cited alongside, same era.
Fairness and Machine Learning
A survey on bias and fairness in machine learning
Mehrabi, N.; Morstatter, F.; Saxena, N.; Lerman, K.; and Galstyan, A. 2021 · 2021
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Differentially private empirical risk minimization under the fairness lens
Tran, C.; Dinh, M.; and Fioretto, F. 2021 · 2021
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A fairness analysis on private aggregation of teacher ensembles
Tran, C.; Dinh, M. H.; Beiter, K.; and Fioretto, F. 2021 · 2021
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Opacus: User-Friendly Differential Privacy Library in PyTorch
Yousefpour, A.; Shilov, I.; Sablayrolles, A.; Testuggine, D.; Prasad, K.; Malek, M.; Nguyen, J.; Ghosh, S.; Bharadwaj, A.; Zhao, J.; Cormode, G.; and Mironov, I. 2021 · 2021
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The causal fairness field guide: Perspectives from social and formal sciences
Carey, A. N.; and Wu, X. 2022 · 2022
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Barocas, S.; Hardt, M.; and Narayanan, A. 2019 · 2019
Cited alongside, same era.
Monte carlo and reconstruction membership inference attacks against generative models
Hilprecht, B.; Härterich, M.; and Bernau, D. 2019 · 2019
Cited alongside, same era.
Differentially private fair learning
Jagielski, M.; Kearns, M.; Mao, J.; Oprea, A.; Roth, A.; Sharifi-Malvajerdi, S.; and Ullman, J. 2019 · 2019
Cited alongside, same era.
Fairness without demographics through adversarially reweighted learning
Lahoti, P.; Beutel, A.; Chen, J.; Lee, K.; Prost, F.; Thain, N.; Wang, X.; and Chi, E. 2020 · 2020
Cited alongside, same era.
Fair decision making using privacy-protected data
Pujol, D.; McKenna, R.; Kuppam, S.; Hay, M.; Machanavajjhala, A.; and Miklau, G. 2020 · 2020
Cited alongside, same era.
On the privacy risks of algorithmic fairness
Chang, H.; and Shokri, R. 2021 · 2021
Cited alongside, same era.
Retiring adult: New datasets for fair machine learning
Ding, F.; Hardt, M.; Miller, J.; and Schmidt, L. 2021 · 2021
Cited alongside, same era.
A clarification of the nuances in the fairness metrics landscape
Castelnovo, A.; Crupi, R.; Greco, G.; Regoli, D.; Penco, I. G.; and Cosentini, A. C. 2022 · 2022
Later among the works it cites.
Dpnas: Neural architecture search for deep learning with differential privacy
Cheng, A.; Wang, J.; Zhang, X. S.; Chen, Q.; Wang, P.; and Cheng, J. 2022 · 2022
Later among the works it cites.
Unlocking high-accuracy differentially private image classification through scale
De, S.; Berrada, L.; Hayes, J.; Smith, S. L.; and Balle, B. 2022 · 2022
Later among the works it cites.
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data
Ganev, G.; Oprisanu, B.; and De Cristofaro, E. 2022 · 2022
Later among the works it cites.
Toward training at imagenet scale with differential privacy
Kurakin, A.; Song, S.; Chien, S.; Geambasu, R.; Terzis, A.; and Thakurta, A. 2022 · 2022
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Exploring the unfairness of DP-SGD across settings
Noe, F.; Herskind, R.; and Søgaard, A. 2022 · 2022
Later among the works it cites.
Hyperparameter Tuning with Renyi Differential Privacy
Papernot, N.; and Steinke, T. 2022 · 2022
Later among the works it cites.
Towards intersectionality in machine learning: Including more identities, handling underrepresentation, and performing evaluation
Wang, A.; Ramaswamy, V. V.; and Russakovsky, O. 2022 · 2022
Later among the works it cites.
Fairfed: Enabling group fairness in federated learning
Ezzeldin, Y. H.; Yan, S.; He, C.; Ferrara, E.; and Avestimehr, A. S. 2023 · 2023
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