Wearable technology is increasingly being used for medical applications such as continuous monitoring of chronically ill patients in homes and hospitals. The various stakeholders (patients, doctors, insurers) have an interest in ensuring not only that the data is untampered, but also that the context is verifiable (e.g., correct time and location can be associated with the data collected). Prior works have studied these aspects in isolation, typically using cryptographic techniques. In this paper, we develop a new solution that leverages the density of wireless devices in the vicinity of the transaction to create witness records ensuring data is tamper-protected and bound to its time and location context. Our first contribution is to develop a secure logging architecture that compacts witness records using Bloom filters and hash-chains them to bind them to the data, allowing fast and reliable forensic verification. Our second contribution is to identify the various configuration parameters influencing the performance of our scheme in terms of storage, processing, and transmission efficiency, and to quantify their effect on verification accuracy. Our third contribution implements and demonstrates the feasibility of our scheme, and quantifies its efficacy via simulation using real trace data from a multi-storey building representing a hospital environment.

中文翻译：

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穿戴式医疗保健传感设备的安全机会性上下文记录

可穿戴技术正越来越多地用于医疗应用，例如对家庭和医院中的慢性病患者进行连续监测。各个利益相关者（患者，医生，保险公司）都有兴趣确保不仅数据不受篡改，而且上下文可验证（例如，正确的时间和位置可以与收集的数据相关联）。先前的工作通常使用加密技术来单独研究这些方面。在本文中，我们开发了一种新的解决方案，该解决方案利用交易附近的无线设备的密度来创建见证记录，以确保数据受到防篡改并绑定到其时间和位置上下文。我们的第一项贡献是开发了一种安全的日志记录体系结构，该体系结构使用Bloom筛选器压缩见证记录，并通过哈希链将它们绑定到数据，从而实现快速可靠的取证验证。我们的第二个贡献是确定在存储，处理和传输效率方面影响我们方案性能的各种配置参数，并量化它们对验证准确性的影响。我们的第三项贡献实现并证明了该方案的可行性，并通过使用代表医院环境的多层建筑中的真实跟踪数据通过仿真来量化其有效性。我们的第二个贡献是确定在存储，处理和传输效率方面影响我们方案性能的各种配置参数，并量化它们对验证准确性的影响。我们的第三项贡献实现并证明了该方案的可行性，并通过使用代表医院环境的多层建筑中的真实跟踪数据通过仿真来量化其有效性。我们的第二个贡献是确定在存储，处理和传输效率方面影响我们方案性能的各种配置参数，并量化它们对验证准确性的影响。我们的第三项贡献实现并证明了该方案的可行性，并通过使用代表医院环境的多层建筑中的真实跟踪数据通过仿真来量化其有效性。