PRISM：隐私保护型医疗物联网安全管理
PRISM: Privacy Preserving Healthcare Internet of Things Security Management
AI总结：
本文提出边缘系统 PRISM，通过自动化 IoT 实验和隐私保护方式评估居家医疗监测的异常检测，并在 44 户 PLWD 两年数据上实现最高 99% 准确率与 0.88 秒训练时间。
摘要：
消费级医疗物联网（IoT）设备正日益普及于我们的家庭和医院。这些设备以低成本提供持续监测，并可用于增强高精度医疗设备。然而，在将预训练全局模型应用于智能健康监测中的异常检测时，面对其所照护的多样化个体，仍存在重大挑战。本文提出 PRISM，这是一个用于开展居家智能医疗设备实验的边缘系统。我们开发了一种依赖自动化 IoT 实验的严谨方法。我们使用了一个丰富的真实世界数据集，该数据集来自两年间对 44 户痴呆症患者（PLWD）家庭的居家患者监测。结果表明，异常识别准确率最高可达 99%，平均训练时间低至 0.88 秒。尽管所有模型在同一患者数据上训练时都能达到高准确率，但在不同患者上评估时，其准确率会下降。
英文摘要：
Consumer healthcare Internet of Things (IoT) devices are gaining popularity in our homes and hospitals. These devices provide continuous monitoring at a low cost and can be used to augment high-precision medical equipment. However, major challenges remain in applying pre-trained global models for anomaly detection on smart health monitoring, for a diverse set of individuals that they provide care for. In this paper, we propose PRISM, an edge-based system for experimenting with in-home smart healthcare devices. We develop a rigorous methodology that relies on automated IoT experimentation. We use a rich real-world dataset from in-home patient monitoring from 44 households of People Living With Dementia (PLWD) over two years. Our results indicate that anomalies can be identified with accuracy up to 99% and mean training times as low as 0.88 seconds. While all models achieve high accuracy when trained on the same patient, their accuracy degrades when evaluated on different patients.
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