DP-CTGAN: Differentially Private Medical Data Generation Using CTGANs

Mei, Fang; Dhami, Devendra Singh; Kersting, Kristian

doi:10.1007/978-3-031-09342-5_17

Cited by 19 publications

(11 citation statements)

References 13 publications

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“…FDP-CTGAN [ 66 ] - it should be noted this paper concentrates on presenting a version of CTGAN [ 17 ] (which is a GAN specifically designed to generate tabular data) with the addition of DP (DP-CTGAN), and a federated version is only briefly described. FDP-CTGAN synthesises tabular medical data, and its performance in terms of utility (predictive performance of a model trained on synthetic data but tested on real data) is compared to other (non-FL) methods; the federated version is outperformed (in terms of, the Area Under Receiver Operating Curve and Area Under the Precision-Recall curve metrics) in all but one case by CTGAN and DP-CTGAN.…”

Section: Resultsmentioning

confidence: 99%

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Federated learning for generating synthetic data: a scoping review

Little,

Elliot,

Allmendinger

2023

IJPDS

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IntroductionFederated Learning (FL) is a decentralised approach to training statistical models, where training is performed across multiple clients, producing one global model. Since the training data remains with each local client and is not shared or exchanged with other clients the use of FL may reduce privacy and security risks (compared to methods where multiple data sources are pooled) and can also address data access and heterogeneity problems. Synthetic data is artificially generated data that has the same structure and statistical properties as the original but that does not contain any of the original data records, therefore minimising disclosure risk. Using FL to produce synthetic data (which we refer to as "federated synthesis") has the potential to combine data from multiple clients without compromising privacy, allowing access to data that may otherwise be inaccessible in its raw format. ObjectivesThe objective was to review current research and practices for using FL to generate synthetic data and determine the extent to which research has been undertaken, the methods and evaluation practices used, and any research gaps. MethodsA scoping review was conducted to systematically map and describe the published literature on the use of FL to generate synthetic data. Relevant studies were identified through online databases and the findings are described, grouped, and summarised. Information extracted included article characteristics, documenting the type of data that is synthesised, the model architecture and the methods (if any) used to evaluate utility and privacy risk. ResultsA total of 69 articles were included in the scoping review; all were published between 2018 and 2023 with two thirds (46) in 2022. 30% (21) were focussed on synthetic data generation as the main model output (with 6 of these generating tabular data), whereas 59% (41) focussed on data augmentation. Of the 21 performing federated synthesis, all used deep learning methods (predominantly Generative Adversarial Networks) to generate the synthetic data. ConclusionsFederated synthesis is in its early days but shows promise as a method that can construct a global synthetic dataset without sharing any of the local client data. As a field in its infancy there are areas to explore in terms of the privacy risk associated with the various methods proposed, and more generally in how we measure those risks.

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Section: Resultsmentioning

confidence: 99%

“…• A. GAN on clients: 7 papers [60,61,63,66,69,73,75] had a GAN on each client but not on the server. Usually, each client trains its GAN, sends the parameters to the server, which aggregates them and sends them back to the client, and so on.…”

Section: What Methods Have Been Used For Federated Synthesis?mentioning

confidence: 99%

Federated learning for generating synthetic data: a scoping review

Little,

Elliot,

Allmendinger

2023

IJPDS

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“…[41] Their analysis conducted on MNIST concludes promising preliminary results in the extension of the DP+GAN framework. Further variation with DP-CTGAN was evaluated by Fang, Dhami, and Kersting, [42] with evaluation on numerous sets of medical tabular data.…”

Section: Differential Privacy and Ctgan In Privacy Preservationmentioning

confidence: 99%

Consumer Data Without Compromise: Integrating Differential Privacy and GANs for Privacy-Preserving Digital Marketing

Chen

2024

AEMPS

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In the rapidly evolving digital marketing landscape, the utilization of consumer data is essential for efficient targeting and personalization of marketing practices. However, the growing concerns regarding user privacy and stringent data protection regulations have created challenges in accessing and using consumer data for marketing purposes. This paper introduces a novel approach that leverages Differential Privacy and Conditional Tabular Generative Adversarial Networks (CTGAN) to address these privacy concerns while maintaining the efficacy of data-driven digital marketing strategies. Our approach amalgamates the strengths of Differential Privacy and CTGAN, applying differential privacy to the original dataset to ensure that extracted data cannot be tied back to individuals. We then train a CTGAN on an open marketing dataset to learn and generate synthetic data of close resemblance. Through extensive empirical analysis, we evaluate the fidelity, utility, and trade-offs of our approach, demonstrating its effectiveness in synthesizing non-Gaussian and multi-modal distributions, and its applicability in real-world classification problems. The research also highlights the complexity of hyperparameter tuning and the importance of a balanced approach in model training. Our findings contribute valuable insights to both the theoretical understanding of generative models and practical guidance for digital marketing practitioners

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“…Differential privacy is an approach where a machine learning (ML) algorithm is designed to safeguard the privacy of individuals whose data is used for training. By integrating differential privacy techniques into the data generation process, synthetic datasets can be created with privacy assurances (10; 11; 13; 21). However, it’s important to note that although differential privacy provides statistical privacy guarantees, the addition of significant noise can reduce the usefulness of the data for ML development, especially when the original data itself may be inherently noisy(22).…”

Section: Related Workmentioning

confidence: 99%

Geometrically-aggregated training samples: Leveraging summary statistics to enable healthcare data democratization

Yang,

Thakur,

Soltan

et al. 2023

Preprint

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Healthcare data is highly sensitive and confidential, with strict regulations and laws to protect patient privacy and security. However, these regulations impede the access of healthcare data to a wider AI research community. As a result, AI healthcare research is often dominated by organisations with access to larger datasets or limited to silo-based development, where models are trained and evaluated on a limited population. Taking inspiration from the non-sensitive nature of the summary statistics (mean, variance, etc.) of healthcare data, this paper proposes geometrically-aggregated training samples (GATS) where each training sample is a convex combination of multiple patients' characteristics. Thus, mappings from patients to any constructed sample are highly convoluted, preserving patient privacy. We demonstrate that these "summary training units" provide effective training on different tabular and time-series datasets (CURIAL, UCI Adult, and eICU), and indeed behave as a summary of the original training datasets. This approach takes important steps towards data accessibility and democratization.

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DP-CTGAN: Differentially Private Medical Data Generation Using CTGANs

Cited by 19 publications

References 13 publications

Federated learning for generating synthetic data: a scoping review

Federated learning for generating synthetic data: a scoping review

Consumer Data Without Compromise: Integrating Differential Privacy and GANs for Privacy-Preserving Digital Marketing

Geometrically-aggregated training samples: Leveraging summary statistics to enable healthcare data democratization

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