  In this technical report, we explore the use of homomorphic encryption (HE)
in the context of training and predicting with deep learning (DL) models to
deliver strict \textit{Privacy by Design} services, and to enforce a zero-trust
model of data governance. First, we show how HE can be used to make predictions
over medical images while preventing unauthorized secondary use of data, and
detail our results on a disease classification task with OCT images. Then, we
demonstrate that HE can be used to secure the training of DL models through
federated learning, and report some experiments using 3D chest CT-Scans for a
nodule detection task.
