PPGAN: Privacy-Preserving Generative Adversarial Network
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 seman...
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| Vydáno v: | 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS) s. 985 - 989 |
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IEEE
01.12.2019
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| 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. |
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| AbstractList | 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. |
| Author | Peng, Jialiang Wu, Yi Yu, James J.Q. Liu, Yi |
| Author_xml | – sequence: 1 givenname: Yi surname: Liu fullname: Liu, Yi organization: Heilongjiang University – sequence: 2 givenname: Jialiang surname: Peng fullname: Peng, Jialiang organization: Heilongjiang University – sequence: 3 givenname: James J.Q. surname: Yu fullname: Yu, James J.Q. organization: Southern University of Science and Technology – sequence: 4 givenname: Yi surname: Wu fullname: Wu, Yi organization: Heilongjiang University |
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| Snippet | Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount... |
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| SubjectTerms | Data models deep learning Differential privacy GAN Generative adversarial networks Mathematical models moments accountant Noise Privacy leakage Scalability Stability analysis Synthetic data Training Training data |
| Title | PPGAN: Privacy-Preserving Generative Adversarial Network |
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