Due to the increasing medical data for coronary heart disease (CHD) diagnosis, how to assist doctors to make proper clinical diagnosis has attracted considerable attention. However, it faces many challenges, including personalized diagnosis, high dimensional datasets, clinical privacy concerns and insufficient computing resources. To handle these issues, we propose a novel blockchain-enabled contextual online learning model under local differential privacy for CHD diagnosis in mobile edge computing. Various edge nodes in the network can collaborate with each other to achieve information sharing, which guarantees that CHD diagnosis is suitable and reliable. To support the dynamically increasing dataset, we adopt a top-down tree structure to contain medical records which is partitioned adaptively. Furthermore, we consider patients’ contexts (e.g., lifestyle, medical history records, and physical features) to provide more accurate diagnosis. Besides, to protect the privacy of patients and medical transactions without any trusted third party, we utilize the local differential privacy with randomised response mechanism and ensure blockchain-enabled information-sharing authentication under multi-party computation. Based on the theoretical analysis, we confirm that we provide real-time and precious CHD diagnosis for patients with sublinear regret, and achieve efficient privacy protection. The experimental results validate that our algorithm outperforms other algorithm benchmarks on running time, error rate and diagnosis accuracy.

中文翻译：

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移动边缘计算中用于冠心病诊断的本地差分隐私下的区块链支持的上下文在线学习。


由于冠心病（CHD）诊断的医疗数据不断增加，如何协助医生做出正确的临床诊断引起了人们的广泛关注。然而，它面临着许多挑战，包括个性化诊断、高维数据集、临床隐私问题和计算资源不足。为了解决这些问题，我们提出了一种新颖的基于区块链的上下文在线学习模型，用于移动边缘计算中的冠心病诊断。网络中的各个边缘节点可以相互协作，实现信息共享，保证了CHD诊断的适用性和可靠性。为了支持动态增长的数据集，我们采用自上而下的树结构来包含自适应分区的医疗记录。此外，我们还会考虑患者的背景（例如生活方式、病史记录和身体特征）以提供更准确的诊断。此外，为了在没有任何可信第三方的情况下保护患者和医疗交易的隐私，我们利用具有随机响应机制的本地差分隐私，并确保多方计算下的区块链信息共享认证。基于理论分析，我们证实我们为亚线性后悔的患者提供实时、有价值的CHD诊断，并实现高效的隐私保护。实验结果验证了我们的算法在运行时间、错误率和诊断准确性方面优于其他算法基准。