Application of Homomorphic Encryption in Medical Imaging
This work addresses privacy concerns in medical data processing for healthcare applications, but it is incremental as it applies existing encryption methods to new domains.
The paper tackled the problem of ensuring privacy in medical imaging by applying homomorphic encryption to deep learning for prediction and training, achieving results on disease classification with OCT images and nodule detection with 3D chest CT-scans.
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.