Data mining (DM) and machine learning (ML) applications in medical diagnostic systems are budding. Data privacy is essential in these systems as healthcare data are highly sensitive. The proposed work first discusses various privacy and security challenges in these systems. To address these next, we discuss different privacy-preserving (PP) computation techniques in the context of DM and ML for secure data evaluation and processing. The state-of-the-art applications of these systems in healthcare are analyzed at various stages such as data collection, data publication, data distribution, and output phases regarding PPDM and input, model, training, and output phases in the context of PPML. Furthermore, PP federated learning is also discussed. Finally, we present open challenges in these systems and future research directions.

中文翻译：

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医疗保健中的隐私保护数据挖掘和机器学习：应用、挑战和解决方案

医疗诊断系统中的数据挖掘 (DM) 和机器学习 (ML) 应用程序正在萌芽。由于医疗保健数据高度敏感，数据隐私在这些系统中至关重要。拟议的工作首先讨论了这些系统中的各种隐私和安全挑战。接下来，为了解决这些问题，我们讨论了 DM 和 ML 上下文中用于安全数据评估和处理的不同隐私保护 (PP) 计算技术。这些系统在医疗保健中的最先进应用在不同阶段进行了分析，例如关于 PPDM 的数据收集、数据发布、数据分发和输出阶段以及 PPML 上下文中的输入、模型、训练和输出阶段. 此外，还讨论了 PP 联邦学习。最后，我们提出了这些系统中的开放挑战和未来的研究方向。