  In this paper we measure the effectiveness of $\epsilon$-Differential Privacy
(DP) when applied to medical imaging. We compare two robust differential
privacy mechanisms: Local-DP and DP-SGD and benchmark their performance when
analyzing medical imagery records. We analyze the trade-off between the model's
accuracy and the level of privacy it guarantees, and also take a closer look to
evaluate how useful these theoretical privacy guarantees actually prove to be
in the real world medical setting.
