AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data

Bae, Ho; Jung, Dahuin; Choi, Hyun-Soo; Yoon, Sungroh

doi:10.1142/9789811215636_0050

Cited by 23 publications

(20 citation statements)

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“…Inter-dimensional correlation Dimension-wise Pearson coefficient correlation matrices for both real and synthetic data (Beaulieu-Jones et al, 2019;Goncalves et al, 2020;Torfi and Beyki, 2019;Frid-Adar et al, 2018;Ozyigit et al, 2020;Yang et al, 2019c;Zhu et al, 2020a;Walsh et al, 2020;Yale et al, 2019a;Ozyigit et al, 2020;Dash et al, 2019;Bae et al, 2020b).…”

Section: Metric Descriptionmentioning

confidence: 99%

“…Training utility Performance of predictors trained on the synthetic data, often in comparison with the real data or data generated with DP (Bae et al, 2020a).…”

Section: Metric Descriptionmentioning

confidence: 99%

“…Accuracy on synthetic data of some form of predictor trained on real data (Bae et al, 2020a;Jordon et al, 2019).…”

Section: Tstrmentioning

confidence: 99%

“…Numerous attempts at applying traditional privacy guarantees, such as deferentially-private stochastic gradient descent can also be found in other fields, as well as in healthcare (Beaulieu-Jones et al, 2019;Esteban et al, 2017;Chin-Cheong et al, 2020;Bae et al, 2020a). By limiting the gradient amplitude at each step and adding random noise, AC-GAN could produce useful data with = 3.5 and δ < 10 −5 according to the definition of differential privacy.…”

Section: Traditional Privacymentioning

confidence: 99%

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Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

Geogres-Filteau¹,

Cirillo

2020

Preprint

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After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it.Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging.The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above.Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics.We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.

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Section: Metric Descriptionmentioning

confidence: 99%

“…Training utility Performance of predictors trained on the synthetic data, often in comparison with the real data or data generated with DP (Bae et al, 2020a).…”

Section: Metric Descriptionmentioning

confidence: 99%

“…Accuracy on synthetic data of some form of predictor trained on real data (Bae et al, 2020a;Jordon et al, 2019).…”

Section: Tstrmentioning

confidence: 99%

Section: Traditional Privacymentioning

confidence: 99%

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Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

Geogres-Filteau¹,

Cirillo

2020

Preprint

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“…Recent advances in generative machine learning such as variational autoencoders (VAEs) and generative adversarial networks (GANs) are starting to be applied to the anonymization problem [18][19][20][21][22][23][24]. The main idea of the generative approach is to learn the salient characteristics of the data distribution and sample new (synthetic) individuals from the distribution.…”

Section: Introductionmentioning

confidence: 99%

Preservation of Anomalous Subgroups On Variational Autoencoder Transformed Data

Maina¹,

Bryant²,

Ogallo³

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the groups predicted odds ratio from the model and observed odds ratio from the data. We then perform anonymization using a variational autoencoder (VAE) to synthesize an entirely new dataset that would ideally be drawn from the distribution of the original data. We repeat the anomalous subgroup discovery task on the new data and compare it to what was identified pre-anonymization. We evaluated our approach using publicly available datasets from the financial industry. Our evaluation confirmed that the approach was able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset. Such a distinction was maintained while having distinctly different records between the synthetic and original dataset. Finally, we packed the above end-to-end process into what we call Utility Guaranteed Deep Privacy (UGDP) system. UGDP can be easily extended to onboard alternative generative approaches such as GANs to synthesize tabular data.

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Low-Effort Re-identification Techniques Based on Medical Imagery Threaten Patient Privacy

Esmeral

Uhl

2022

Medical Image Understanding and Analysis

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AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data

Cited by 23 publications

References 0 publications

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

Preservation of Anomalous Subgroups On Variational Autoencoder Transformed Data

Low-Effort Re-identification Techniques Based on Medical Imagery Threaten Patient Privacy

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