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Failed to load latest commit information. Explicitly replace "import tensorflow" with "tensorflow.compat.v1" Feb 7, 2020 Explicitly replace "import tensorflow" with "tensorflow.compat.v1" Feb 7, 2020 Explicitly replace "import tensorflow" with "tensorflow.compat.v1" Feb 7, 2020 Explicitly replace "import tensorflow" with "tensorflow.compat.v1" Feb 7, 2020 Explicitly replace "import tensorflow" with "tensorflow.compat.v1" Feb 7, 2020 Explicitly replace "import tensorflow" with "tensorflow.compat.v1" Feb 7, 2020

BoltOn Subpackage

This package contains source code for the BoltOn method, a particular differential-privacy (DP) technique that uses output perturbations and leverages additional assumptions to provide a new way of approaching the privacy guarantees.

BoltOn Description

This method uses 4 key steps to achieve privacy guarantees:

  1. Adds noise to weights after training (output perturbation).
  2. Projects weights to R, the radius of the hypothesis space, after each batch. This value is configurable by the user.
  3. Limits learning rate
  4. Uses a strongly convex loss function (see compile)

For more details on the strong convexity requirements, see: Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics by Xi Wu et al. at

Why BoltOn?

The major difference for the BoltOn method is that it injects noise post model convergence, rather than noising gradients or weights during training. This approach requires some additional constraints listed in the Description. Should the use-case and model satisfy these constraints, this is another approach that can be trained to maximize utility while maintaining the privacy. 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 no noising.


This package has a tutorial that can be found in the root tutorials directory, under


This package was initially contributed by Georgian Partners with the hope of growing the tensorflow/privacy library. There are several rich use cases for delta-epsilon privacy in machine learning, some of which can be explored here:


As we are pegged on tensorflow2.0, this package may encounter stability issues in the ongoing development of tensorflow2.0.

This sub-package is currently stable for 2.0.0a0, 2.0.0b0, and 2.0.0.b1 If you would like to use this subpackage, please do use one of these versions as we cannot guarantee it will work for all latest releases. If you do find issues, feel free to raise an issue to the contributors listed below.


In addition to the maintainers of tensorflow/privacy listed in the root, please feel free to contact members of Georgian Partners. In particular,

  • Georgian Partners(@georgianpartners)
  • Ji Chao Zhang(@Jichaogp)
  • Christopher Choquette(@cchoquette)


Copyright 2019 - Google LLC

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