mlsanityData to 14 Oct 2026

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

arXiv 2303.15563 ↗First posted 27 Mar 2023cs.LG cs.CRcited by 4

Not on the map. Why?

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.

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

28 of 81 references.

References · 28

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  6. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  7. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 156
  8. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 128
  9. 2020Secure and Robust Machine Learning for Healthcare: A Surveycited by 80
  10. 2022Integrated multimodal artificial intelligence framework for healthcare applicationscited by 71
  11. 2020When Machine Learning Meets Privacy: A Survey and Outlookcited by 68
  12. 2020Leakage of Dataset Properties in Multi-Party Machine Learningcited by 47
16 more
  1. 2022Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imagingcited by 39
  2. 2020Chasing Your Long Tails: Differentially Private Prediction in Health Care Settingscited by 37
  3. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  4. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  5. 2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17
  6. 2022Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Casecited by 11
  7. 2021A methodology for training homomorphicencryption friendly neural networkscited by 10
  8. 2021PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Settingcited by 9
  9. 2022Towards the Practical Utility of Federated Learning in the Medical Domaincited by 9
  10. 2020Privacy-preserving medical image analysiscited by 4
  11. 2020Privacy-preserving collaborative machine learning on genomic data using TensorFlowcited by 3
  12. 2022Report: State of the Art Solutions for Privacy Preserving Machine Learning in the Medical Contextcited by 3
  13. 2022Privacy-Preserving Deep Learning Model for Covid-19 Disease Detectioncited by 3
  14. 2022Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approachcited by 2
  15. 2022Scalable privacy-preserving cancer type prediction with homomorphic encryptioncited by 2
  16. 2021Privacy-preserving Machine Learning for Medical Image Classificationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Reviewcited by 2
  2. 2025Towards integration of Privacy Enhancing Technologies in Explainable Artificial Intelligencecited by 1
  3. 2026LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settingscited by 0
  4. 2026HEART: A High-Efficiency Adaptive Real-Time Telemonitoring Framework for Secure Electrocardiogram Signal Transmission Using Chaotic Encryptioncited by 0