Benchmarking Differentially Private Residual Networks for Medical Imagery
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Hospitals and other medical institutions often have vast amounts of medical data which can provide significant value when utilized to advance research. However, this data is often sensitive in nature, and as such is not readily available for use in a research setting, often due to privacy concerns. In this paper, we measure the performance of a deep neural network on differentially private image datasets pertaining to Pneumonia. We analyze the trade-off between the model's accuracy and the scale of perturbation among the images. Knowing how the model's accuracy varies among various perturbation levels in differentially private medical images is useful in these contexts. This work is contextually significant given the corona-virus pandemic, as Pneumonia has become an even greater concern owing to its potentially deadly complication of infection with COVID-19.
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