Differentially Private Synthetic Medical Data Generation using Convolutional GANs

Abstract

Deep learning models have demonstrated superior performance in several application problems, such as image classification and speech processing. However, creating a deep learning model using health record data requires addressing certain privacy challenges that bring unique concerns to researchers working in this domain. One effective way to handle such private data issues is to generate realistic synthetic data that can provide practically acceptable data quality and correspondingly the model performance. To tackle this challenge, we develop a differentially private framework for synthetic data generation using Rényi differential privacy. Our approach builds on convolutional autoencoders and convolutional generative adversarial networks to preserve some of the critical characteristics of the generated synthetic data. In addition, our model can also capture the temporal information and feature correlations that might be present in the original data. We demonstrate that our model outperforms existing state-of-the-art models under the same privacy budget using several publicly available benchmark medical datasets in both supervised and unsupervised settings.

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Cited by in corpus (12)

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• Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging
• Large Language Models Can Be Strong Differentially Private Learners
• Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare
• Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey
• Multi-Label Clinical Time-Series Generation via Conditional GAN
• Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
• Diffusion Models for Tabular Data Imputation and Synthetic Data Generation
• Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis
• DTGAN: Differential Private Training for Tabular GANs
• Effective and Privacy preserving Tabular Data Synthesizing
• IRG: Modular Synthetic Relational Database Generation with Complex Relational Schemas