# Application of Homomorphic Encryption in Medical Imaging

arXiv: https://arxiv.org/abs/2110.07768 · HTML: https://mlsanity.com/paper/2110.07768/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [Gazelle: A Low Latency Framework for Secure Neural Network Inference](https://mlsanity.com/paper/1801.05507/) (2018)
- [Low Latency Privacy Preserving Inference](https://mlsanity.com/paper/1812.10659/) (2018)
- [XONN: XNOR-based Oblivious Deep Neural Network Inference](https://mlsanity.com/paper/1902.07342/) (2019)
- [OCTID: Optical Coherence Tomography Image Database](https://mlsanity.com/paper/1812.07056/) (2018)
- [A review of homomorphic encryption and software tools for encrypted statistical machine learning](https://mlsanity.com/paper/1508.06574/) (2015)

## Built on by (most cited first, 2 of 2)

- [Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges](https://mlsanity.com/paper/2510.23883/) (2025)
- [Seven Security Challenges in Cross-domain Multi-agent LLM Systems](https://mlsanity.com/paper/2505.23847/) (2025)

## 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.