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privacy/bolton/README.md
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@ -5,7 +5,7 @@ of methods used in the ensuring privacy in machine learning that leverages
additional assumptions to provide a new way of approaching the privacy additional assumptions to provide a new way of approaching the privacy
guarantees. guarantees.
# Bolton Description ## Bolton Description
This method uses 4 key steps to achieve privacy guarantees: This method uses 4 key steps to achieve privacy guarantees:
1. Adds noise to weights after training (output perturbation). 1. Adds noise to weights after training (output perturbation).
@ -17,7 +17,7 @@ For more details on the strong convexity requirements, see:
Bolt-on Differential Privacy for Scalable Stochastic Gradient Bolt-on Differential Privacy for Scalable Stochastic Gradient
Descent-based Analytics by Xi Wu et al. Descent-based Analytics by Xi Wu et al.
# Why Bolton? ## Why Bolton?
The major difference for the Bolton method is that it injects noise post model The major difference for the Bolton method is that it injects noise post model
convergence, rather than noising gradients or weights during training. This convergence, rather than noising gradients or weights during training. This
@ -28,12 +28,12 @@ The paper describes in detail the advantages and disadvantages of this approach
and its results compared to some other methods, namely noising at each iteration and its results compared to some other methods, namely noising at each iteration
and no noising. and no noising.
# Tutorials ## Tutorials
This package has a tutorial that can be found in the root tutorials directory, This package has a tutorial that can be found in the root tutorials directory,
under `bolton_tutorial.py`. under `bolton_tutorial.py`.
# Contribution ## Contribution
This package was initially contributed by Georgian Partners with the hope of This package was initially contributed by Georgian Partners with the hope of
growing the tensorflow/privacy library. There are several rich use cases for growing the tensorflow/privacy library. There are several rich use cases for
@ -41,7 +41,7 @@ delta-epsilon privacy in machine learning, some of which can be explored here:
https://medium.com/apache-mxnet/epsilon-differential-privacy-for-machine-learning-using-mxnet-a4270fe3865e https://medium.com/apache-mxnet/epsilon-differential-privacy-for-machine-learning-using-mxnet-a4270fe3865e
https://arxiv.org/pdf/1811.04911.pdf https://arxiv.org/pdf/1811.04911.pdf
# Contacts ## Contacts
In addition to the maintainers of tensorflow/privacy listed in the root In addition to the maintainers of tensorflow/privacy listed in the root
README.md, please feel free to contact members of Georgian Partners. In README.md, please feel free to contact members of Georgian Partners. In
@ -51,6 +51,6 @@ particular,
* Ji Chao Zhang(@Jichaogp) * Ji Chao Zhang(@Jichaogp)
* Christopher Choquette(@cchoquette) * Christopher Choquette(@cchoquette)
# Copyright ## Copyright
Copyright 2019 - Google LLC Copyright 2019 - Google LLC