Application of Homomorphic Encryption in Medical Imaging
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Abstract (arXiv)
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 textitPrivacy 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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 12 Oct 2021.
Builds on
8 of 26 references.
References · 8
- 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
- 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
- 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
- 2018Gazelle: A Low Latency Framework for Secure Neural Network Inferencecited by 193
- 2018Low Latency Privacy Preserving Inferencecited by 57
- 2019XONN: XNOR-based Oblivious Deep Neural Network Inferencecited by 51
- 2018OCTID: Optical Coherence Tomography Image Databasecited by 36
- 2015A review of homomorphic encryption and software tools for encrypted statistical machine learningcited by 9
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Citing papers per year: 2025: 2.
2 papers.