These are the codes used in the papers titled: (1) Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning (https://arxiv.org/abs/1709.05750), (2) Preserving Differential Privacy in Convolutional Deep Belief Networks (https://arxiv.org/abs/1706.08839), and (3) Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction (https://dl.acm.org/citation.cfm?id=3016005).
The software is written in tensorflow. It requires the following packages:
python3
Tensorflow 1.1 or later
Compute differentially private LRP for MNIST:
python dpLRP_MNIST.py
Compute differentially private LRP for Cifar10:
python3 dpLRP_Cifar10.py
Run AdLM on MNIST:
python3 AdLM.py
Run AdLM on Cifar10:
python3 AdLMCNN_CIFAR.py
Run evaluation of AdLM on Cifar10:
python3 CifarEval_AdLM2.py
Run pCDBN:
python3 pcdbn.py
Run dp-Autoencoder:
python3 dpautoencoder.py
Run dpSGD on MNIST:
python3 DPSGD_CNN.py
Run dpSGD on Cifar10:
python3 pSGDCNN_CIFAR.py
Run dpSGD evaluation on Cifar10:
python3 CifarEval.py
The default hyper-parameters can be edited in each main() file.
If you use this code, please cite the following papers:
Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning NhatHai Phan, Xintao Wu, Han Hu, Dejing Dou. https://arxiv.org/abs/1709.05750.
Preserving Differential Privacy in Convolutional Deep Belief Networks NhatHai Phan, Xintao Wu, Dejing Dou. https://arxiv.org/abs/1706.08839.
Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction NhatHai Phan, Yue Wang, Xintao Wu, and Dejing Dou. Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16).