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Privacy-Aware Sensor Data Upload Management for Securely Receiving Smart Home Services

机译:隐私感知传感器数据上传管理,可安全接收智能家居服务

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Recently smart homes equipped with many sensors and IoT devices are widespread. However, when smart home users receive smart home services like elderly monitoring, they need to upload their privacy sensitive data to potentially untrusted cloud servers where the service quality (user's benefit) depends on the amount/frequency of the uploaded data. In this paper, aiming to minimize the risk of privacy leakage and maximize users' benefit obtained through services, we propose a novel privacy-aware data management method that works on a smart-home system composed of smart homes with sensors, edge computing servers, and a cloud server. We formulate a combinatorial optimization problem which determines the best choice of data type (raw or activity label recognized at the edge) and upload frequency in each time slot taking into account the constraints of edge server resources and users' budgets as well as the k-anonymity of activities and users' preferences. Since the target problem is NP-hard, we propose a heuristic algorithm to derive semi-optimal solutions by determining choices with better objective function values in a greedy manner. Through experiments using smart-home open dataset, we confirmed that the proposed method outperforms the conventional methods using only a cloud server.
机译:近年来,配备了许多传感器和物联网设备的智能家居正在广泛普及。但是,当智能家居用户接收诸如老人监控之类的智能家居服务时,他们需要将其隐私敏感数据上传到潜在不受信任的云服务器,其中服务质量(用户的利益)取决于上传数据的数量/频率。在本文中,为了最大程度地减少隐私泄露的风险并通过服务获得最大的用户利益,我们提出了一种新颖的隐私感知数据管理方法,该方法可在由智能家居,传感器,边缘计算服务器,和一台云服务器。我们制定了一个组合优化问题,考虑到边缘服务器资源和用户预算以及k-活动的匿名性和用户的偏好。由于目标问题是NP难解的,因此我们提出一种启发式算法,以贪婪的方式通过确定具有较好目标函数值的选择来得出半最优解。通过使用智能家居开放数据集的实验,我们证实了所提出的方法优于仅使用云服务器的常规方法。

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