Skip to main content

Privacy preserving deep learning for PyTorch

Project description


PyVacy: Privacy Algorithms for PyTorch

PyVacy provides custom PyTorch opimizers for conducting deep learning in a differentially private manner. Basically TensorFlow Privacy, but for PyTorch.

Getting Started

pip install pyvacy

Example Usage

import torch
from pyvacy import optim, analysis

model = torch.nn.Sequential(...)

optimizer = optim.DPSGD(
    l2_norm_clip=...,
    noise_multiplier=...,
    batch_size=...,
    lr=...,
    momentum=...,
)

epsilon = analysis.moments_accountant(
    N=...,
    batch_size=...
    noise_multiplier=...,
    epochs=...,
    delta=...,
)

for epoch in range(epochs):
    # do training as usual...

Tutorials

python tutorials/mnist.py

Training procedure achieves (3.0, 0.00001)-DP
[Epoch 1/60] [Batch 0/235] [Loss: 2.321049]
[Epoch 1/60] [Batch 10/235] [Loss: 0.952795]
[Epoch 1/60] [Batch 20/235] [Loss: 1.040896]
...

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pyvacy, version 0.0.32
Filename, size File type Python version Upload date Hashes
Filename, size pyvacy-0.0.32.tar.gz (10.3 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page