Generating Privacy Preserving Synthetic Medical Data

Abstract

Due to the recent development in the deep learning community and the availability of state-of-the-art models, medical practitioners are getting more interested in computer vision and deep learning for diagnosis tasks. Moreover, those medical diagnostic models can also increase the reliability of conventional findings. As radiology images can convey a lot of information for a patient’s diagnosis task, the problem is that such medical data may contain sensitive private information in their content header. De-anonymization (i.e., removal of sensitive header information) does not work well due to the re-identification risk, which may link those images to essential details (e.g., birth date, SSN, institution name, etc.), and such an approach can also reduce utility. In the medical domain, utility is significant because a less accurate diagnosis may lead to the wrong course of treatment and/or loss of life. In this paper, we developed a differentially private approach that can generate high-quality and high dimensional synthetic medical image data with guaranteed differential privacy. It can be used to create sufficient quality data to train a deep model. Moreover, we used W-GAN for bounded gradient guarantee, which eliminates the need for an extensive clipping hyperparameter search. We also added noise selectively to the generator to maintain the privacy-utility trade-off. Due to a noise-free discriminator and such selective noise addition to the generator, high-quality and reliable generated radiology images can be utilized for diagnosis tasks. Moreover, our approach can work in a distributed system where different hospitals can contain their private images in the local server and use a central server to generate synthetic radiology images without storing patient data.

Topics & Concepts