Privacy-preserving machine learning for healthcare: open challenges and future perspectives. (arXiv:2303.15563v1 [cs.LG])

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, …

as-a-service challenges data discuss disease focus future healthcare identify literature machine machine learning medical medical data modeling model training nature opportunities perspectives pipeline prediction privacy research review service training trends