Open AccessProceedings Article
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees
James Jordon,Jinsung Yoon,Mihaela van der Schaar +2 more
- 27 Sep 2018
TL;DR: This paper investigates a method for ensuring (differential) privacy of the generator of the Generative Adversarial Nets (GAN) framework, and modifies the Private Aggregation of Teacher Ensembles (PATE) framework and applies it to GANs.
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Abstract: Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available. Unfortunately, much of that potential is not being realized because it would require sharing data in a way that compromises privacy. In this paper, we investigate a method for ensuring (differential) privacy of the generator of the Generative Adversarial Nets (GAN) framework. The resulting model can be used for generating synthetic data on which algorithms can be trained and validated, and on which competitions can be conducted, without compromising the privacy of the original dataset. Our method modifies the Private Aggregation of Teacher Ensembles (PATE) framework and applies it to GANs. Our modified framework (which we call PATE-GAN) allows us to tightly bound the influence of any individual sample on the model, resulting in tight differential privacy guarantees and thus an improved performance over models with the same guarantees. We also look at measuring the quality of synthetic data from a new angle; we assert that for the synthetic data to be useful for machine learning researchers, the relative performance of two algorithms (trained and tested) on the synthetic dataset should be the same as their relative performance (when trained and tested) on the original dataset. Our experiments, on various datasets, demonstrate that PATE-GAN consistently outperforms the stateof-the-art method with respect to this and other notions of synthetic data quality.
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Citations
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
TL;DR: In this article , the authors consider the use of neural tangent kernels (NTKs) to distinguish between real and synthetic data distributions, and find that the expressiveness of the untrained e-NTK features is comparable to that of the features taken from pre-trained perceptual features using public data.
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Algorithmically Effective Differentially Private Synthetic Data
Yi He,Roman Vershynin,Yizhe Zhu +2 more
TL;DR: In this article , the authors presented a highly effective algorithm for generating differentially private synthetic data in a bounded metric space with near-optimal utility guarantees under the 1-Wasserstein distance.
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•Posted Content
Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees.
Aleksei Triastcyn,Boi Faltings +1 more
TL;DR: This paper proposes employing Bayesian differential privacy as the means to achieve a rigorous theoretical guarantee while providing a better privacy-utility trade-off, and demonstrates experimentally that this approach produces higher-fidelity samples.
Meticulously Selecting 1% of the Dataset for Pre-training! Generating Differentially Private Images Data with Semantics Query
Kecen Li,Chen Gong,Zhixiang Li,Yuzhong Zhao,Xinwen Hou,Tianhao Wang +5 more
TL;DR: A novel DP image synthesis method, termed PRIVIMAGE, which meticulously selects pre-training data, promoting the efficient creation of DP datasets with high fidelity and utility and achieves superior synthetic performance and conserves more computational resources.
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FCT-GAN: Enhancing Table Synthesis via Fourier Transform
TL;DR: A Fourier conditional tabular generative adversarial network (FCT-GAN) is proposed that can synthesize tabular data with high machine learning utility and high statistical similarity to the original data, while maintaining the global correlation across columns, especially on high dimensional dataset.
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