TL;DR Benchmark private inference and training on standard neural networks using Function Secret Sharing.
This is the open-source implementation for the AriaNN framework paper:
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
by Théo Ryffel, Pierre Tholoniat, David Pointcheval, Francis Bach
Benchmark private evaluation of Resnet18 on one batch of size 2 with preprocessing.
python main.py --model resnet18 --dataset hymenoptera --batch_size 2 --preprocess
Compute the test accuracy of a pretrained Alexnet on Tiny Imagenet and compare it to its plain text accuracy.
python main.py --model alexnet --dataset tiny-imagenet --batch_size 32 --test
Train a small CNN on MNIST for 15 epochs and compute accuracy achieved.
python main.py --model network2 --dataset mnist --train --epochs 15 --lr 0.01
usage: main.py [-h] [--model MODEL] [--dataset DATASET] [--batch_size BATCH_SIZE] [--test_batch_size TEST_BATCH_SIZE] [--preprocess] [--fp_only] [--public] [--test] [--train] [--epochs EPOCHS]
[--lr LR] [--momentum MOMENTUM] [--websockets] [--verbose] [--log_interval LOG_INTERVAL] [--comm_info] [--pyarrow_info]
optional arguments:
-h, --help show this help message and exit
--model MODEL model to use for inference (network1, network2, lenet, alexnet, vgg16, resnet18)
--dataset DATASET dataset to use (mnist, cifar10, hymenoptera, tiny-imagenet)
--batch_size BATCH_SIZE
size of the batch to use. Default 128.
--test_batch_size TEST_BATCH_SIZE
size of the batch to use
--preprocess [only for speed test] preprocess data or not
--fp_only Don't secret share values, just convert them to fix precision
--public [needs --train] Train without fix precision or secret sharing
--test run testing on the complete test dataset
--train run training for n epochs
--epochs EPOCHS [needs --train] number of epochs to train on. Default 15.
--lr LR [needs --train] learning rate of the SGD. Default 0.01.
--momentum MOMENTUM [needs --train] momentum of the SGD. Default 0.9.
--websockets use PyGrid nodes instead of a virtual network. (nodes are launched automatically)
--verbose show extra information and metrics
--log_interval LOG_INTERVAL
[needs --test or --train] log intermediate metrics every n batches. Default 10.
--comm_info Print communication information
--pyarrow_info print information about PyArrow usage and failure
docker-compose up
Connect to the container:
docker exec -ti ariann /bin/bash
You can already start running the first experiments! Try:
python main.py --model alexnet --dataset cifar10 --batch_size 128 --preprocess
To reproduce the paper experiments, see the USAGE.md page.
To perform cross-node execution (the WAN setting), you will need to install from source instead.
Alternatively, you can also install the code from source.
We assume the AriaNN code has been put at the home directory: ~/ariann
Download PySyft from GitHub using the ryffel/ariaNN branch and install in editable mode:
cd ~
git clone https://github.com/OpenMined/PySyft.git
cd PySyft
git checkout a73b13aa84a8a9ad0923d87ff1b6c8c2facdeaa6
pip install -e .
You can already start running the first experiments! Try:
cd ~/ariann
python main.py --model alexnet --dataset cifar10 --batch_size 128 --preprocess
But there are still some extras steps to have the whole setup!
To run experiments across different nodes, download PyGrid from GitHub.
cd ~
git clone https://github.com/OpenMined/PyGrid.git
cd PyGrid
git checkout 1ded5901ce643c1abadd23f54cc84b9c25438f5b
Install poetry
curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -
Take a fresh tab, install with poetry (it might take a minute or two):
cd ~/PyGrid/apps/node
poetry install
poetry env info -p
Copy paste the path output, say <path>. Now reinstall manually PySyft in the env.
source <path>/bin/activate
cd ~/PySyft
pip install -e .
deactivate
Verify that the paths are correct in scripts/launch_***.sh:
cd ~/ariann
nano scripts/launch_alice.sh
nano scripts/launch_bob.sh
nano scripts/launch_crypto_provider.sh
Verify that the HOME path is correct:
nano data.py
There are 2 datasets that you need to install manually in your HOME:
- Hymenoptera using the instructions:
cd ~ wget https://download.pytorch.org/tutorial/hymenoptera_data.zip unzip hymenoptera_data.zip - Tiny Imagenet, from https://github.com/tjmoon0104/pytorch-tiny-imagenet:
cd ~ git clone https://github.com/tjmoon0104/pytorch-tiny-imagenet.git cd pytorch-tiny-imagenet pip install opencv-python ./run.sh
The working directory is: cd ~/ariann
When you first launch the workers, you might have a SQL error. That's not important, just re-run the experiment and it will be gone.
Also, we recommend always testing an experiment locally (ie without --websockets) before using websockets, to make sure everything runs fine.
1 x 28 x 28 pixel images
Suitability: Network1, Network2, LeNet
3 x 32 x32 pixel images
Suitability: AlexNet and VGG16
3 x 64 x 64 pixel images
Suitability: AlexNet and VGG16
3 x 224 x 224 pixel images
Suitability: ResNet18
If you're enthusiastic about our project, ⭐️ it to show your support! ❤️