  Diagnostic radiologists need artificial intelligence (AI) for medical
imaging, but access to medical images required for training in AI has become
increasingly restrictive. To release and use medical images, we need an
algorithm that can simultaneously protect privacy and preserve pathologies in
medical images. To develop such an algorithm, here, we propose DP-GLOW, a
hybrid of a local differential privacy (LDP) algorithm and one of the
flow-based deep generative models (GLOW). By applying a GLOW model, we
disentangle the pixelwise correlation of images, which makes it difficult to
protect privacy with straightforward LDP algorithms for images. Specifically,
we map images onto the latent vector of the GLOW model, each element of which
follows an independent normal distribution, and we apply the Laplace mechanism
to the latent vector. Moreover, we applied DP-GLOW to chest X-ray images to
generate LDP images while preserving pathologies.
