# `deepee`

`deepee` is a library for differentially private deep learning in PyTorch. More precisely, `deepee` implements the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm originally described by [Abadi et al.](https://arxiv.org/pdf/1607.00133.pdf). Despite the name, `deepee` works with any (first order) optimizer, including Adam, AdaGrad, etc. 

It wraps a regular `PyTorch` model and takes care of calculating per-sample gradients, clipping, noising and accumulating gradients with an API which closely mimics the `PyTorch` API of the original model.

Check out the documentation [here](http://g-k.ai/deepee/)

# For paper readers
If you would like to reproduce the results from our paper, please go [here](https://github.com/gkaissis/deepee/tree/results)

Fast (and cheeky) differentially private gradient-based optimisation in PyTorch. 

Socket Acquires Coana to Bring Reachability Analysis to Every Appsec Team

deepee

Fast (and cheeky) differentially private gradient-based optimisation in PyTorch

g.kaissis

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deepee

For paper readers

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