# Privacy-preserving medical image analysis

arXiv: https://arxiv.org/abs/2012.06354 · HTML: https://mlsanity.com/paper/2012.06354/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods](https://mlsanity.com/paper/2103.03078/) (2021)
- [Privacy-preserving machine learning for healthcare: open challenges and future perspectives](https://mlsanity.com/paper/2303.15563/) (2023)
- [A Prototype-Oriented Clustering for Domain Shift with Source Privacy](https://mlsanity.com/paper/2302.03807/) (2023)
- [Erase to Retain: Low Rank Adaptation Guided Selective Unlearning in Medical Segmentation Networks](https://mlsanity.com/paper/2511.16574/) (2025)

## Abstract (arXiv)

The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains. The conflict between data usage and privacy protection requirements in such systems must be resolved for optimal results as well as ethical and legal compliance. This calls for innovative solutions such as privacy-preserving machine learning (PPML). We present PriMIA (Privacy-preserving Medical Image Analysis), a software framework designed for PPML in medical imaging. In a real-life case study we demonstrate significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets. Furthermore, we show an inference-as-a-service scenario for end-to-end encrypted diagnosis, where neither the data nor the model are revealed. Lastly, we empirically evaluate the framework's security against a gradient-based model inversion attack and demonstrate that no usable information can be recovered from the model.