Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics

Analytic Inequalities , volume 1

Mitrinovic, D. S. and Vasic, P. M · 1970

Earlier work this paper cites.

Logarithmic Sobolev inequalities

Gross, L · 1975

Earlier work this paper cites.

Multinomial logistic regression algorithm

Böhning, D · 1992

Earlier work this paper cites.

Sparse multinomial logistic regression: Fast algorithms and generalization bounds

Krishnapuram, B., Carin, L., Figueiredo, M. A., and Hartemink, A. J · 2005

Earlier work this paper cites.

Boosting and differential privacy

Dwork, C., Rothblum, G. N., and Vadhan, S · 2010

Earlier work this paper cites.

Bayesian learning via stochastic gradient Langevin dynamics

Welling, M. and Teh, Y. W · 2011

Earlier work this paper cites.

Imagenet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012

Earlier work this paper cites.

A stochastic gradient method with an exponential convergence rate for finite training sets

Roux, N., Schmidt, M., and Bach, F · 2012

Cited alongside, same era.

The algorithmic foundations of differential privacy

Dwork, C., Roth, A., et al · 2014

Cited alongside, same era.

The CIFAR-10 dataset

Krizhevsky, A., Nair, V., and Hinton, G · 2014

Cited alongside, same era.

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.

Deep residual learning for image recognition

He, K., Zhang, X., Ren, S., and Sun, J · 2016

Cited alongside, same era.

Rényi differential privacy

Mironov, I · 2017

Cited alongside, same era.

Identifying medical diagnoses and treatable diseases by image-based deep learning

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al · 2018

Later among the works it cites.

Differential privacy dynamics of langevin diffusion and noisy gradient descent

Chourasia, R., Ye, J., and Shokri, R · 2021

Later among the works it cites.

Crypten: Secure multi-party computation meets machine learning

Knott, B., Venkataraman, S., Hannun, A., Sengupta, S., Ibrahim, M., and van der Maaten, L · 2021

Later among the works it cites.

Differentially private learning needs better features (or much more data)

Tramèr, F. and Boneh, D · 2021

Later among the works it cites.

FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning

Wagh, S., Tople, S., Benhamouda, F., Kushilevitz, E., Mittal, P., and Rabin, T · 2021

Later among the works it cites.