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The code reproduces the results of the experiments in the paper. In particular, it performs experiments in which machine-learning models are trained that are guaranteed to not leak information about the training data they are trained on.

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Private Prediction

This repository contains code that can be used to reproduce the experimental results presented in the paper:

Installation

The code requires Python 3.5+, PyTorch 1.5.0+, torchvision 0.6.0+, and visdom (optional). It also uses parts of TensorFlow Privacy and pytorch_resnet_cifar10.

Presuming you have installed Anaconda, you can install all the dependencies via:

conda install -c pytorch pytorch torchvision
pip install visdom
python install.py

Usage

All experiments can be run via the private_prediction_experiment.py script. For example, to train and test a linear model on the MNIST dataset using loss perturbation with privacy loss 1.0, you can use the following command:

python private_prediction_experiment.py \
    --dataset mnist \
    --method loss_perturbation \
    --epsilon 1.0

The following input arguments can be used to change the model, private prediction method, and privacy loss:

  • --model: the model used can be linear (default) or resnet{20,32,44,56,110,1202}
  • --method: private prediction method can be subsagg (default), loss_perturbation, {model,logit}_sensitivity, or dpsgd
  • --epsilon: privacy loss value for predictions (default = infinity)
  • --delta: privacy failure probability for predictions (default = 0.0)
  • --inference_budget: number of inferences to support (default = -1 to try many values)
  • --weight_decay: L2-regularization parameter (default = 0.0; set to -1 to cross-validate)

The following input arguments can be used to change details of the optimizer:

  • --optimizer: optimizer used can be lbfgs (default) or sgd
  • --num_epochs: number of training epochs (default = 100)
  • --batch_size: batch size for SGD optimization (default = 32)
  • --learning_rate: initial learning rate for SGD optimization (default = 1.0)

The following input arguments alter hyperparameters of specific private prediction methods:

  • --num_models: number of models in subsample-and-aggregate method (default = 32)
  • --noise_dist: noise distribution used in sensitivity methods can be sqrt_gaussian (default), laplacian, gaussian, advanced_gaussian
  • --clip: gradient clipping value for DP-SGD (default = 1e-1; set to -1 to cross-validate)
  • --use_lr_scheduler: use learning rate reduction (for DP-SGD)

The following input arguments alter the dataset used for experimentation:

  • --dataset: the dataset used can be mnist (default), mnist1m, cifar10, or cifar100
  • --num_samples: number of training samples to use (default: all)
  • --num_classes: number of classes to use (default: all)
  • --pca_dims: number of PCA dimensions for data (default: PCA not used)

The following input arguments alter other system properties:

  • --device: compute device can be cpu (default) or gpu
  • --visdom: visdom server for learning curves (default = localhost)
  • --num_repetitions: number of times to repeat experiment (default = 10)
  • --data_folder: folder in which to store dataset for re-use
  • --result_file: file in which to write experimental results (default: unused)

Using the MNIST-1M Dataset

The MNIST-1M dataset used in the paper is not directly available for download, but can be generated using InfiniMNIST.

Download InfiniMNIST and run:

make
mkdir /tmp/infinimnist
infimnist patterns 70000 1069999 > /tmp/infinimnist/mnist1m-images-idx3-ubyte
infimnist labels 70000 1069999 > /tmp/infinimnist/mnist1m-labels-idx1-ubyte
infimnist patterns 0 9999 > t10k-images-idx3-ubyte
infimnist labels 0 9999 > t10k-labels-idx1-ubyte

Now, you should be able to run experiments on the MNIST-1M dataset, for example:

python private_prediction_experiment.py \
    --dataset mnist1m \
    --num_samples 100000 \
    --method loss_perturbation \
    --epsilon 1.0 \
    --data_folder /tmp/infinimnist

Citing this Repository

If you use the code in this repository, please cite the corresponding paper:

License

This code is released under a CC-BY-NC 4.0 license. Please see the LICENSE file for more information.

Please review Facebook Open Source Terms of Use and Privacy Policy.

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The code reproduces the results of the experiments in the paper. In particular, it performs experiments in which machine-learning models are trained that are guaranteed to not leak information about the training data they are trained on.

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