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PPGAN: Privacy-preserving Generative Adversarial Network | | Yi Liu
; Jialiang Peng
; James J.Q Yu
; Yi Wu
; | | Date: |
4 Oct 2019 | | Abstract: | Generative Adversarial Network (GAN) and its variants serve as a perfect
representation of the data generation model, providing researchers with a large
amount of high-quality generated data. They illustrate a promising direction
for research with limited data availability. When GAN learns the semantic-rich
data distribution from a dataset, the density of the generated distribution
tends to concentrate on the training data. Due to the gradient parameters of
the deep neural network contain the data distribution of the training samples,
they can easily remember the training samples. When GAN is applied to private
or sensitive data, for instance, patient medical records, as private
information may be leakage. To address this issue, we propose a
Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we
achieve differential privacy in GANs by adding well-designed noise to the
gradient during the model learning procedure. Besides, we introduced the
Moments Accountant strategy in the PPGAN training process to improve the
stability and compatibility of the model by controlling privacy loss. We also
give a mathematical proof of the differential privacy discriminator. Through
extensive case studies of the benchmark datasets, we demonstrate that PPGAN can
generate high-quality synthetic data while retaining the required data
available under a reasonable privacy budget. | | Source: | arXiv, 1910.2007 | | Services: | Forum | Review | PDF | Favorites | |
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