uds-lsv / SIDP

Robust Differentially Private Training of Deep Neural Networks

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SIDP

The PyTorch implementation of 'Robust Differentially Private Training of Deep Neural Networks'. https://arxiv.org/abs/2006.10919

Usage

To run the code on MNIST and CIFAR-10 datasets, execute vision.py with desired parameters:

python vision.py 

For the text classification model based on the AGNews Corpus:

python main_text_class.py [experiment_name] [noise_std] [clip]
python main_text_class.py sidp 0.3 7

Results

MNIST

Privacy epsilon 7 3 1 0.5 0.1 0.05 0.025
DPSGD (LeNet5) 99.2 97 96.34 94.11 91.1 83.0 78.96 31.56
SI-DPSGD (LeNet5) 99.2 98.9 98.9 98.72 99.1 99.0 98.84 90.82
SI-DPSGD (BN-LeNet5) 99.2 99.17 99.17 99.15 99.18 99.14 99.12 98.58

CIFAR-10

Privacy epsilon 8 4 2 1 0.5 0.1 0.05
DPSGD (TF-tutorial) 80.0 73.0 70.0 67.0 NA NA NA NA
SI-DPSGD (TF-tutorial) 80.0 78.10 77.70 76.0 76.05 74.20 73.80 74.05
SI-DPSGD (ResNet-18) 93.50 90.20 90.16 90.26 90.09 89.67 84.88 84.47

AGNews Text Classification

Privacy epsilon 7 3 1 0.5 0.1 0.05
DPSGD (BiLSTM-DL) 88.5 83.9 80.0 81.1 77.9 37.5 31.8
SI-DPSGD (BiLSTM-DL) 88.5 85.9 85.7 83.3 81.2 77.9 56.7
DPSGD (LN-BiLSTM-DL) 88.5 83.5 82.4 82.0 78.9 50.1 31.6
SI-DPSGD (LN-BiLSTM-DL) 88.5 87.8 87.6 85.7 85.4 84.3 80.1

References

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Robust Differentially Private Training of Deep Neural Networks

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