# Privacy-preserving machine learning for healthcare: open challenges and future perspectives

arXiv: https://arxiv.org/abs/2303.15563 · HTML: https://mlsanity.com/paper/2303.15563/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 81 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Secure and Robust Machine Learning for Healthcare: A Survey](https://mlsanity.com/paper/2001.08103/) (2020)
- [Integrated multimodal artificial intelligence framework for healthcare applications](https://mlsanity.com/paper/2202.12998/) (2022)
- [When Machine Learning Meets Privacy: A Survey and Outlook](https://mlsanity.com/paper/2011.11819/) (2020)
- [Leakage of Dataset Properties in Multi-Party Machine Learning](https://mlsanity.com/paper/2006.07267/) (2020)
- [Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging](https://mlsanity.com/paper/2205.08576/) (2022)
- [Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings](https://mlsanity.com/paper/2010.06667/) (2020)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2203.09702/) (2022)
- [Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training](https://mlsanity.com/paper/2111.01338/) (2021)
- [Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case](https://mlsanity.com/paper/2204.07752/) (2022)
- [A methodology for training homomorphicencryption friendly neural networks](https://mlsanity.com/paper/2111.03362/) (2021)
- [PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting](https://mlsanity.com/paper/2102.09751/) (2021)
- [Towards the Practical Utility of Federated Learning in the Medical Domain](https://mlsanity.com/paper/2207.03075/) (2022)
- [Privacy-preserving medical image analysis](https://mlsanity.com/paper/2012.06354/) (2020)
- [Privacy-preserving collaborative machine learning on genomic data using TensorFlow](https://mlsanity.com/paper/2002.04344/) (2020)
- [Report: State of the Art Solutions for Privacy Preserving Machine Learning in the Medical Context](https://mlsanity.com/paper/2201.11406/) (2022)
- [Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection](https://mlsanity.com/paper/2209.04445/) (2022)
- [Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach](https://mlsanity.com/paper/2203.07728/) (2022)
- [Scalable privacy-preserving cancer type prediction with homomorphic encryption](https://mlsanity.com/paper/2204.05496/) (2022)
- [Privacy-preserving Machine Learning for Medical Image Classification](https://mlsanity.com/paper/2108.12816/) (2021)

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

- [Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review](https://mlsanity.com/paper/2505.02828/) (2025)
- [Towards integration of Privacy Enhancing Technologies in Explainable Artificial Intelligence](https://mlsanity.com/paper/2507.04528/) (2025)
- [LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings](https://mlsanity.com/paper/2602.18934/) (2026)
- [HEART: A High-Efficiency Adaptive Real-Time Telemonitoring Framework for Secure Electrocardiogram Signal Transmission Using Chaotic Encryption](https://mlsanity.com/paper/2605.08456/) (2026)

## Abstract (arXiv)

Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this paper, we conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service, and perform a comprehensive review of existing trends, identify challenges, and discuss opportunities for future research directions. The aim of this review is to guide the development of private and efficient ML models in healthcare, with the prospects of translating research efforts into real-world settings.