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

TL;DR

This paper reviews recent advances in privacy-preserving machine learning for healthcare, highlighting current trends, challenges, and future research directions to enable secure and efficient medical data analysis.

Contribution

It provides a comprehensive overview of privacy-preserving techniques in healthcare ML, identifying key challenges and proposing future research opportunities.

Findings

Review of current privacy-preserving ML methods in healthcare

Identification of key challenges in privacy and efficiency

Discussion of future research directions and opportunities

Abstract

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.