  With the rising use of Machine Learning (ML) and Deep Learning (DL) in
various industries, the medical industry is also not far behind. A very simple
yet extremely important use case of ML in this industry is for image
classification. This is important for doctors to help them detect certain
diseases timely, thereby acting as an aid to reduce chances of human judgement
error. However, when using automated systems like these, there is a privacy
concern as well. Attackers should not be able to get access to the medical
records and images of the patients. It is also required that the model be
secure, and that the data that is sent to the model and the predictions that
are received both should not be revealed to the model in clear text.
  In this study, we aim to solve these problems in the context of a medical
image classification problem of detection of pneumonia by examining chest x-ray
images.
