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.

python setup.py install

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

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)

• 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

>>> 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()

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

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)

>>> 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()

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

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

• 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

>>> 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)

The MIT License