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BlurNN - Machine Learning with Differential Privacy

BlurNN is a pytorch-based model privacy-preserving module. Two kinds of mechanisms are included, optimizers with differential privacy and model sanitizers, and differs from the timing of perturb adjunction.

Install

python setup.py install

Usage

Optimizers

Check here for torch.optim usage. This package extends optimizers in torch.optim by extra parameters for differential privacy.

DPSGD

Extended optimizer from torch.optim.SGD. Implements sanitizer in Deep Learning with Differential Privacy.

blurnn.optim.DPSGD(params, lr=<required parameter>, momentum=0, dampening=0, weight_decay=0, nesterov=False, noise_scale=0, norm_bound=0)

Extra Parameters

• norm_bound (float, optional) - gradient cliping bound (gradient * argmax(norm_bound, norm(gradient)))
• noise_scale (float, optional) - gaussian noise with standard deviation noise_scale * norm_bound is added to each clipped gradient

Usage

>>> optimizer = dp_optim.SGD(model.parameters(), lr=0.1, momentum=0.9, noise_scale=0.01, norm_bound=1.5)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()

Performance

Following graphs are comparison of loss and accuracy during epochs using different optimizers over MNIST.

DPAdam

blurnn.optim.DPAdam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False, noise_scale=0, norm_bound=0)

Usage

>>> optimizer = dp_optim.DPAdam(model.parameters(), noise_scale=0.01, norm_bound=1.5)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()

Performance

Following graphs are comparison of loss and accuracy during epochs using different optimizers over MNIST.

Model Sanitizer

ModelSanitizer

Implement Sparse Vector Technique(SVT) to obtain (eps1 + eps2 + eps3)-differential privacy Reference Privacy-preserving Federated Brain Tumour Segmentation

Parameters

• epochs (int):
• eps1 (float):
• eps2 (float, optional): default: (2 * release_proportion * sensitivity) ^ (2/3) * eps1
• eps3 (float):
• release_proportion (float, optional): the proportion of model to be shared. release_proportion should be in range [0,1), default: 0.4
• sensitivity (float, optional): default: 1
• gamma (float, optional): the bound default: 0.5
• tau (float, optional): default: 0.1

Usage

>>> sanitizer = ModelSanitizer(epochs=N, eps1=0.001, eps3=0.001)
>>> sanitizer.base_model = model
>>> for epoch in range(N):
>>>     training...
>>>     testing...
>>>     step...
>>> sanitizer.sanitize(model)

License

The MIT License