├── .gitignore ├── LICENSE ├── README.md ├── dpgan ├── dpgan.py ├── gradient_noise.py └── privacy_accountant.py ├── environment.yml ├── requirements.txt └── resources ├── example_best.png ├── example_moderate.png └── example_worst.png /.gitignore: -------------------------------------------------------------------------------- 1 | # Custom 2 | images/ 3 | models/ 4 | .envrc 5 | 6 | # Byte-compiled / optimized / DLL files 7 | __pycache__/ 8 | *.py[cod] 9 | *$py.class 10 | 11 | # C extensions 12 | *.so 13 | 14 | # Distribution / packaging 15 | .Python 16 | build/ 17 | develop-eggs/ 18 | dist/ 19 | downloads/ 20 | eggs/ 21 | .eggs/ 22 | lib/ 23 | lib64/ 24 | parts/ 25 | sdist/ 26 | var/ 27 | wheels/ 28 | *.egg-info/ 29 | .installed.cfg 30 | *.egg 31 | MANIFEST 32 | 33 | # PyInstaller 34 | # Usually these files are written by a python script from a template 35 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 36 | *.manifest 37 | *.spec 38 | 39 | # Installer logs 40 | pip-log.txt 41 | pip-delete-this-directory.txt 42 | 43 | # Unit test / coverage reports 44 | htmlcov/ 45 | .tox/ 46 | .coverage 47 | .coverage.* 48 | .cache 49 | nosetests.xml 50 | coverage.xml 51 | *.cover 52 | .hypothesis/ 53 | .pytest_cache/ 54 | 55 | # Translations 56 | *.mo 57 | *.pot 58 | 59 | # Django stuff: 60 | *.log 61 | local_settings.py 62 | db.sqlite3 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # pyenv 81 | .python-version 82 | 83 | # celery beat schedule file 84 | celerybeat-schedule 85 | 86 | # SageMath parsed files 87 | *.sage.py 88 | 89 | # Environments 90 | .env 91 | .venv 92 | env/ 93 | venv/ 94 | ENV/ 95 | env.bak/ 96 | venv.bak/ 97 | 98 | # Spyder project settings 99 | .spyderproject 100 | .spyproject 101 | 102 | # Rope project settings 103 | .ropeproject 104 | 105 | # mkdocs documentation 106 | /site 107 | 108 | # mypy 109 | .mypy_cache/ 110 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Differentially Private GANs 2 | 3 | 4 | 5 | MNIST images generated for varying levels of noise. 6 | 7 | ## Getting Started 8 | 9 | If you're using conda, you can create and activate the required environment via the following commands. 10 | 11 | ``` 12 | conda env create -f environment.yml 13 | source activate dpgan 14 | ``` 15 | 16 | ## Running 17 | 18 | ``` 19 | python dpgan/dpgan.py 20 | ``` 21 | 22 | -------------------------------------------------------------------------------- /dpgan/dpgan.py: -------------------------------------------------------------------------------- 1 | import os 2 | os.environ["KERAS_BACKEND"] = "tensorflow" 3 | 4 | import numpy as np 5 | from tqdm import tqdm 6 | import matplotlib.pyplot as plt 7 | 8 | from keras.layers import Input 9 | from keras.models import Model, Sequential 10 | from keras.layers.core import Reshape, Dense, Dropout, Flatten 11 | from keras.layers.advanced_activations import LeakyReLU 12 | from keras.layers.convolutional import Convolution2D, UpSampling2D 13 | from keras.layers.normalization import BatchNormalization 14 | from keras.datasets import mnist 15 | from keras.optimizers import Adam, SGD 16 | from keras import backend as K 17 | from keras import initializers 18 | 19 | from gradient_noise import add_gradient_noise 20 | NoisyAdam = add_gradient_noise(Adam) 21 | 22 | K.set_image_dim_ordering('th') 23 | 24 | np.random.seed(0) # Deterministic output. 25 | random_dim = 100 # For consistency with other GAN implementations. 26 | 27 | # Load data 28 | (X_train, y_train), (X_test, y_test) = mnist.load_data() 29 | X_train = (X_train.astype(np.float32) - 127.5) / 127.5 30 | X_train = X_train.reshape(60000, 784) 31 | 32 | # Generator 33 | generator = Sequential() 34 | generator.add(Dense(256, input_dim=random_dim, kernel_initializer=initializers.RandomNormal(stddev=0.02))) 35 | generator.add(LeakyReLU(0.2)) 36 | generator.add(Dense(512)) 37 | generator.add(LeakyReLU(0.2)) 38 | generator.add(Dense(1024)) 39 | generator.add(LeakyReLU(0.2)) 40 | generator.add(Dense(X_train.shape[1], activation='tanh')) 41 | 42 | generator_optimizer = Adam(lr=0.0002, beta_1=0.5) 43 | generator.compile(optimizer=generator_optimizer, loss='binary_crossentropy') 44 | 45 | # Discriminator 46 | discriminator = Sequential() 47 | discriminator.add(Dense(1024, input_dim=X_train.shape[1], kernel_initializer=initializers.RandomNormal(stddev=0.02))) 48 | discriminator.add(LeakyReLU(0.2)) 49 | discriminator.add(Dropout(0.3)) 50 | discriminator.add(Dense(512)) 51 | discriminator.add(LeakyReLU(0.2)) 52 | discriminator.add(Dropout(0.3)) 53 | discriminator.add(Dense(256)) 54 | discriminator.add(LeakyReLU(0.2)) 55 | discriminator.add(Dropout(0.3)) 56 | discriminator.add(Dense(1, activation='sigmoid')) 57 | 58 | clipnorm = 5.0 59 | standard_deviation = 0.0001 60 | discriminator_optimizer = NoisyAdam(lr=0.0002, beta_1=0.5, clipnorm=clipnorm, standard_deviation=standard_deviation) 61 | discriminator.compile(optimizer=discriminator_optimizer, loss='binary_crossentropy') 62 | 63 | # GAN 64 | discriminator.trainable = False 65 | gan_input = Input(shape=(random_dim,)) 66 | x = generator(gan_input) 67 | gan_output = discriminator(x) 68 | gan = Model(inputs=gan_input, outputs=gan_output) 69 | 70 | gan_optimizer = Adam(lr=0.0002, beta_1=0.5) 71 | gan.compile(optimizer=gan_optimizer, loss='binary_crossentropy') 72 | 73 | # Losses for plotting 74 | discriminator_losses = [] 75 | generator_losses = [] 76 | 77 | def plot_loss(epoch): 78 | plt.figure(figsize=(10, 8)) 79 | plt.plot(discriminator_losses, label='Discriminitive Loss') 80 | plt.plot(generator_losses, label='Generative Loss') 81 | plt.xlabel('Epoch') 82 | plt.ylabel('Loss') 83 | plt.legend() 84 | plt.savefig('images/gan_loss_epoch_{}.png'.format(epoch)) 85 | 86 | def plot_generated_images(epoch, examples=100, dim=(10, 10), figsize=(10, 10)): 87 | noise = np.random.normal(0, 1, size=[examples, random_dim]) 88 | generated_images = generator.predict(noise) 89 | generated_images = generated_images.reshape(examples, 28, 28) 90 | 91 | plt.figure(figsize=figsize) 92 | for i in range(generated_images.shape[0]): 93 | plt.subplot(dim[0], dim[1], i+1) 94 | plt.imshow(generated_images[i], interpolation='nearest', cmap='gray_r') 95 | plt.axis('off') 96 | 97 | plt.tight_layout() 98 | plt.savefig('images/gan_generated_image_epoch_{}.png'.format(epoch)) 99 | 100 | def save_models(epoch): 101 | generator.save('models/gan_generator_epoch_{}.h5'.format(epoch)) 102 | discriminator.save('models/gan_discriminator_epoch_{}.h5'.format(epoch)) 103 | 104 | def train(epochs=1, batch_size=128): 105 | batch_count = int(X_train.shape[0] / batch_size) 106 | 107 | for e in range(1, epochs+1): 108 | print('-' * 15, 'Epoch {}'.format(e), '-' * 15) 109 | for _ in tqdm(range(batch_count)): 110 | # Get a random set of input noise and images 111 | noise = np.random.normal(0, 1, size=[batch_size, random_dim]) 112 | image_batch = X_train[np.random.randint(0, X_train.shape[0], size=batch_size)] 113 | 114 | # Generate fake MNIST images 115 | generated_images = generator.predict(noise) 116 | # print np.shape(image_batch), np.shape(generated_images) 117 | X = np.concatenate([image_batch, generated_images]) 118 | 119 | # Labels for generated and real data 120 | y_dis = np.zeros(2 * batch_size) 121 | # One-sided label smoothing 122 | y_dis[:batch_size] = 0.9 123 | 124 | # Train discriminator 125 | discriminator.trainable = True 126 | discriminator_loss = discriminator.train_on_batch(X, y_dis) 127 | 128 | # Train generator 129 | noise = np.random.normal(0, 1, size=[batch_size, random_dim]) 130 | y_gen = np.ones(batch_size) 131 | discriminator.trainable = False 132 | generator_loss = gan.train_on_batch(noise, y_gen) 133 | 134 | # Store loss of most recent batch from this epoch 135 | discriminator_losses.append(discriminator_loss) 136 | generator_losses.append(generator_loss) 137 | 138 | plot_generated_images(e) 139 | if e == 1 or e % 20 == 0: 140 | save_models(e) 141 | 142 | # Plot losses from every epoch 143 | plot_loss(e) 144 | 145 | if __name__ == '__main__': 146 | for path in ['images', 'models']: 147 | if not os.path.exists(path): 148 | os.makedirs(path) 149 | 150 | train(200, 128) 151 | 152 | -------------------------------------------------------------------------------- /dpgan/gradient_noise.py: -------------------------------------------------------------------------------- 1 | import inspect 2 | import keras 3 | from keras import backend as K 4 | 5 | 6 | def _get_shape(x): 7 | if hasattr(x, 'dense_shape'): 8 | return x.dense_shape 9 | 10 | return K.shape(x) 11 | 12 | 13 | def add_gradient_noise(BaseOptimizer): 14 | if not ( 15 | inspect.isclass(BaseOptimizer) and 16 | issubclass(BaseOptimizer, keras.optimizers.Optimizer) 17 | ): 18 | raise ValueError( 19 | 'add_gradient_noise() expects a valid Keras optimizer' 20 | ) 21 | 22 | class NoisyOptimizer(BaseOptimizer): 23 | def __init__(self, standard_deviation=0.3, **kwargs): 24 | super(NoisyOptimizer, self).__init__(**kwargs) 25 | with K.name_scope(self.__class__.__name__): 26 | self.standard_deviation = K.variable(standard_deviation, name='standard_deviation') 27 | 28 | def get_gradients(self, loss, params): 29 | grads = super(NoisyOptimizer, self).get_gradients(loss, params) 30 | 31 | t = K.cast(self.iterations, K.dtype(grads[0])) 32 | 33 | grads = [ 34 | grad + K.random_normal( 35 | _get_shape(grad), 36 | mean=0.0, 37 | stddev=self.standard_deviation, 38 | dtype=K.dtype(grads[0]) 39 | ) 40 | for grad in grads 41 | ] 42 | 43 | return grads 44 | 45 | def get_config(self): 46 | config = {'standard_deviation': float(K.get_value(self.standard_deviation))} 47 | base_config = super(NoisyOptimizer, self).get_config() 48 | return dict(list(base_config.items()) + list(config.items())) 49 | 50 | NoisyOptimizer.__name__ = 'Noisy{}'.format(BaseOptimizer.__name__) 51 | 52 | return NoisyOptimizer 53 | 54 | -------------------------------------------------------------------------------- /dpgan/privacy_accountant.py: -------------------------------------------------------------------------------- 1 | import math 2 | 3 | def epsilon(sensitivity=1, sigma=0.001, delta=0.001, iterations=1): 4 | """ 5 | Given injected Gaussian noise with fixed covariance matrix sig^2, find the epsilon privacy parameters. 6 | We are comparing between two distribution N(mu_1,sigma^2 I) and N(mu_2,sigma^2 I) where 7 | mu = mu_2 - mu_1 is sensitivity when we do a descent step. 8 | - sensitivity: 2-norm of sensitivity. 9 | This is usually the product of the clipping value (bounding two norm of gradient) and learning rate. By default: 1.0 10 | - sigma: standard deviation of Gaussian noise with covariance matrix sigma^2 I_d to be added to comparing distributions. 11 | This is usually the deviation of Gaussian to the gradient multiplied by learning rate. By default: 0.001 12 | - delta: privacy parameter. Must be between 0 and 1. By default: 0.001 13 | - iterations: number of iterations that the algorithm has run this Gaussian mechanism. By default: 1 14 | """ 15 | mu = sensitivity 16 | t = iterations 17 | return math.sqrt(2 * t * math.log(1 / delta) / (sigma ** 2)) * mu + t * (mu ** 2) / (2 * sigma ** 2) 18 | 19 | -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: dpgan 2 | channels: 3 | - defaults 4 | dependencies: 5 | - _tflow_select=2.3.0=mkl 6 | - absl-py=0.6.1=py36_0 7 | - astor=0.7.1=py36_0 8 | - blas=1.0=mkl 9 | - ca-certificates=2018.03.07=0 10 | - certifi=2018.10.15=py36_0 11 | - cycler=0.10.0=py36hfc81398_0 12 | - freetype=2.9.1=hb4e5f40_0 13 | - gast=0.2.0=py36_0 14 | - grpcio=1.12.1=py36hd9629dc_0 15 | - h5py=2.8.0=py36h878fce3_3 16 | - hdf5=1.10.2=hfa1e0ec_1 17 | - intel-openmp=2019.0=118 18 | - keras=2.2.4=0 19 | - keras-applications=1.0.6=py36_0 20 | - keras-base=2.2.4=py36_0 21 | - keras-preprocessing=1.0.5=py36_0 22 | - kiwisolver=1.0.1=py36h0a44026_0 23 | - libcxx=4.0.1=hcfea43d_1 24 | - libcxxabi=4.0.1=hcfea43d_1 25 | - libedit=3.1.20170329=hb402a30_2 26 | - libffi=3.2.1=h475c297_4 27 | - libgfortran=3.0.1=h93005f0_2 28 | - libpng=1.6.35=ha441bb4_0 29 | - libprotobuf=3.6.1=hd9629dc_0 30 | - markdown=3.0.1=py36_0 31 | - matplotlib=3.0.1=py36h54f8f79_0 32 | - mkl=2019.0=118 33 | - mkl_fft=1.0.6=py36hb8a8100_0 34 | - mkl_random=1.0.1=py36h5d10147_1 35 | - ncurses=6.1=h0a44026_0 36 | - numpy=1.15.3=py36h6a91979_0 37 | - numpy-base=1.15.3=py36h8a80b8c_0 38 | - openssl=1.1.1=h1de35cc_0 39 | - pip=18.1=py36_0 40 | - protobuf=3.6.1=py36h0a44026_0 41 | - pyparsing=2.2.2=py36_0 42 | - python=3.6.7=haf84260_0 43 | - python-dateutil=2.7.5=py36_0 44 | - pytz=2018.7=py36_0 45 | - pyyaml=3.13=py36h1de35cc_0 46 | - readline=7.0=h1de35cc_5 47 | - scipy=1.1.0=py36h28f7352_1 48 | - setuptools=40.5.0=py36_0 49 | - six=1.11.0=py36_1 50 | - sqlite=3.25.2=ha441bb4_0 51 | - tensorboard=1.11.0=py36hdc36e2c_0 52 | - tensorflow=1.11.0=mkl_py36h44b7a51_0 53 | - tensorflow-base=1.11.0=mkl_py36h70e0e9a_0 54 | - termcolor=1.1.0=py36_1 55 | - tk=8.6.8=ha441bb4_0 56 | - tornado=5.1.1=py36h1de35cc_0 57 | - tqdm=4.28.1=py36h28b3542_0 58 | - werkzeug=0.14.1=py36_0 59 | - wheel=0.32.2=py36_0 60 | - xz=5.2.4=h1de35cc_4 61 | - yaml=0.1.7=hc338f04_2 62 | - zlib=1.2.11=hf3cbc9b_2 63 | prefix: /Users/chriswaites/miniconda3/envs/dpgan 64 | 65 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==0.6.1 2 | astor==0.7.1 3 | certifi==2018.10.15 4 | cycler==0.10.0 5 | gast==0.2.0 6 | grpcio==1.12.1 7 | h5py==2.8.0 8 | Keras==2.2.4 9 | Keras-Applications==1.0.6 10 | Keras-Preprocessing==1.0.5 11 | kiwisolver==1.0.1 12 | Markdown==3.0.1 13 | matplotlib==3.0.1 14 | mkl-fft==1.0.6 15 | mkl-random==1.0.1 16 | numpy==1.15.3 17 | protobuf==3.6.1 18 | pyparsing==2.2.2 19 | python-dateutil==2.7.5 20 | pytz==2018.7 21 | PyYAML==3.13 22 | scipy==1.1.0 23 | six==1.11.0 24 | tensorboard==1.11.0 25 | tensorflow==1.11.0 26 | termcolor==1.1.0 27 | tornado==5.1.1 28 | tqdm==4.28.1 29 | Werkzeug==0.14.1 30 | -------------------------------------------------------------------------------- /resources/example_best.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/miranthajayatilake/dpgan/d750b94189be8696c7eaade725f1e95b415552af/resources/example_best.png -------------------------------------------------------------------------------- /resources/example_moderate.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/miranthajayatilake/dpgan/d750b94189be8696c7eaade725f1e95b415552af/resources/example_moderate.png -------------------------------------------------------------------------------- /resources/example_worst.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/miranthajayatilake/dpgan/d750b94189be8696c7eaade725f1e95b415552af/resources/example_worst.png --------------------------------------------------------------------------------