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.

翻译：近年来，机器学习在各类医疗预测任务中取得了巨大成功，覆盖从疾病诊断、预后到患者治疗等多个领域。由于医疗数据具有敏感特性，必须在从模型训练到推理的整个机器学习流程中考虑隐私保护问题。本文对近期关于医疗领域隐私保护机器学习的研究文献进行了综述。我们重点关注隐私保护的训练和推理即服务，全面梳理了现有研究趋势，识别了当前面临的挑战，并探讨了未来研究方向的潜在机遇。本综述旨在指导医疗领域开发兼具隐私性与高效性的机器学习模型，推动研究成果向实际场景转化。