PyVertical

A project developing Privacy Preserving Vertically Distributed Learning.

• 🔒 Links vertically partitioned data without exposing membership using Private Set Intersection (PSI)
• 👁️ Trains a model on vertically partitioned data using SplitNNs, so only data holders can access data

PyVertical process:

1. Create partitioned dataset
   • Simulate real-world partitioned dataset by splitting MNIST into a dataset of images and a dataset of labels
   • Give each data point (image + label) a unique ID
   • Randomly shuffle each dataset
   • Randomly remove some elements from each dataset
2. Link datasets using PSI
   • Use PSI to link indices in each dataset using unique IDs
   • Reorder datasets using linked indices
3. Train a split neural network
   • Hold both datasets in a dataloader
   • Send images to first part of split network
   • Send labels to second part of split network
   • Train the network

Requirements

This project is written in Python. The work is displayed in jupyter notebooks.

To install the dependencies, we recommend using Conda:

1. Clone this repository
2. In the command line, navigate to your local copy of the repository
3. Run conda env create -f environment.yml
   • This creates an environment pyvertical-dev
   • Comes with most dependencies you will need
4. Activate the environment with conda activate pyvertical-dev
5. Run pip install syft[udacity]
6. Run conda install notebook

N.b. Installing the dependencies takes several steps to circumvent versioning incompatibility between syft and jupyter. In the future, all packages will be moved into the environment.yml.

Usage

To create a vertically partitioned dataset:

from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor

from src.dataloader import PartitionDistributingDataLoader
from src.dataset import add_ids, partition_dataset

# Create dataset
data = add_ids(MNIST)(".", download=True, transform=ToTensor())  # add_ids adds unique IDs to data points

# Split data
data_partition1, data_partition2 = partition_dataset(data)

# Batch data
dataloader = PartitionDistributingDataLoader(data_partition1, data_partition2, batch_size=128)

for (data, ids1), (labels, ids2) in dataloader:
    # Train a model
    pass

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Read the OpenMined contributing guidelines and styleguide for more information.

Contributors

TTitcombe	Pavlos-p	H4LL

Testing

We use pytest to test the source code. To run the tests manually:

1. In the command line, navigate to the root of this repository
2. Run python -m pytest

CI also checks the code conforms to flake8 standards and black formatting

License

Apache License 2.0