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Differential Privacy (DP) is the leading approach to privacy preserving deep learning.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
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
Earlier work this paper cites.
Privacy Odometers and Filters: Pay-as-You-Go Composition
Rogers, R., Roth, A., Ullman, J., and Vadhan, S · 2016
Cited alongside, same era.
Accuracy first: Selecting a differential privacy level for accuracy constrained erm
Ligett, K., Neel, S., Roth, A., Waggoner, B., and Wu, S. Z · 2017
Cited alongside, same era.
Rényi Differential Privacy
Mironov, I · 2017
Cited alongside, same era.
https://github.com/google/differential-privacy/
Google Differential Privacy
Cited in the paper.
https://diffprivlib.readthedocs.io/en/latest/
IBM Differential Privacy Library
Cited in the paper.
Train PyTorch models with Differential Privacy
Opacus
Cited in the paper.
https://smartnoise.org/
OpenDP
Cited in the paper.
https://github.com/tensorflow/privacy
TensorFlow Privacy
Cited in the paper.
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform
Lécuyer, M., Spahn, R., Vodrahalli, K., Geambasu, R., and Hsu, D · 2019
Later among the works it cites.
Individual Privacy Accounting via a Renyi Filter
Feldman, V. and Zrnic, T · 2020
Later among the works it cites.
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