DEPA for Training is a techno-legal framework that enables privacy-preserving sharing of bulk, de-identified datasets for large scale analytics and training. This repository contains a reference implementation of Confidential Clean Rooms, which together with the Contract Service, forms the basis of this framework. The reference implementation is provided on an As-Is basis. It is work-in-progress and should not be used in production.

The simplest way to setup a development environment is using GitHub Codespaces. The repository includes a devcontainer.json, which customizes your codespace to install all required dependencies. Please ensure you allocate at least 64GB disk space in your codespace. Also, run the following command in the codespace to update submodules.

git submodule update --init --recursive

Alternatively, you can build and develop locally in a Linux environment (we have tested with Ubuntu 20.04 and 22.04), or Windows with WSL 2. Install the following dependencies.

• docker and docker-compose. After installing docker, add your user to the docker group using sudo usermod -aG docker $USER, and log back in to a shell.
• make (install using sudo apt-get install make)
• Python 3.6.9 and pip
• Python wheel package (install using pip install wheel)

Clone this repo as follows.

git clone --recursive http://github.com/iSPIRT/depa-training

To build your own CCR container images, use the following command from the root of the repository.

./ci/build.sh

This scripts build the following containers.

• depa-training: Container with the core CCR logic for joining datasets and running differentially private training.
• depa-training-encfs: Container for loading encrypted data into the CCR.

Alternatively, you can use pre-built container images from the ispirt repository by setting the following environment variable.

export CONTAINER_REGISTRY=ispirt

This repository contains two samples that illustrate the kinds of scenarios DEPA for Training can support.

• Training a differentially private COVID prediction model on private datasets
• Convolutional Neural Network training on MNIST dataset

Follow these links to build and deploy these scenarios.

This project welcomes feedback and contributions. Before you start, please take a moment to review our Contribution Guidelines. These guidelines provide information on how to contribute, set up your development environment, and submit your changes.

We look forward to your contributions and appreciate your efforts in making DEPA Training better for everyone.