Internet of Things (IoT) is increasingly being used in real life, especially in the eHealthcare field. Among eHealthcare, the application of predicting patients’ health status based on their daily activity data which is collected by IoT equipment has attracted extensive attentions and researches. In this application, patients’ data which are treated as time-series data are transmitted to healthcare center (HC), then HC makes predictions based on an established classification model. However, making predictions using classification models requires a lot of computing resources, while HC usually cannot afford such numerous calculations. The use of the cloud solves the problem of insufficient computing resources, but it causes another problem, namely the leakage of user privacy. In particular, not only patients’ data leak patients’ privacy information, the classification model also causes the privacy disclosure of patients and HC. Therefore, we propose a method to store patients’ data and classification model in multiple clouds respectively. According to the method, we design a new system model and propose an algorithm which can protect patients’ data and classification model from leakage and offload calculation to multiple clouds. Our algorithm can better protect privacy of patients and HC in more complex classification scene, and can effectively reduce the computational cost of the healthcare center.

中文翻译：

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多云电子医疗保健中的隐私保护分类


物联网（IoT）越来越多地应用于现实生活中，特别是在电子医疗领域。在电子医疗中，根据物联网设备收集的患者日常活动数据来预测患者健康状况的应用引起了广泛的关注和研究。在此应用中，患者的数据被视为时间序列数据，被传输到医疗中心（HC），然后HC根据已建立的分类模型进行预测。然而，使用分类模型进行预测需要大量的计算资源，而HC通常无法承担如此大量的计算。云的使用解决了计算资源不足的问题，但也带来了另一个问题，即用户隐私的泄露。特别是，不仅患者数据泄露了患者的隐私信息，分类模型也造成了患者和HC的隐私泄露。因此，我们提出了一种将患者数据和分类模型分别存储在多个云中的方法。根据该方法，我们设计了一种新的系统模型，并提出了一种算法，可以保护患者数据和分类模型免遭泄漏和卸载计算到多个云。我们的算法可以在更复杂的分类场景中更好地保​​护患者和HC的隐私，并且可以有效降低医疗中心的计算成本。