2020

Differentially Private Synthetic Medical Data Generation using Convolutional GANs

Torfi, Amirsina, Fox, Edward A., Reddy, Chandan K.

Understand

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 R\'enyi differential privacy.

Built on

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