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On Admitting Sensor Fault Tolerance While Achieving Secure Biosignal Data Sharing

机译：在实现安全生物共享的同时承认传感器容错

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摘要

Remote health monitoring BASNs promise substantive improvements in the quality of healthcare by providing access to diagnostically rich patient data in real-time. However, adoption is hindered by the threat of compromise of the diagnostic quality of the data by faults. Simultaneously, unresolved issues exist with the secure sharing of the sensitive medical data measured by automated BASNs, stemming from the need to provide the data owner (BASN user / patient) and the data consumers (healthcare providers, insurance companies, medical research facilities) secure control over the medical data as it is shared. We address these issues with a robust watermarking approach constrained to leave primary data semantic metrics unaffected and secondary metrics affected minimally. Further, the approach is coordinated with a fault tolerant sensor partitioning technique to afford high semantic accuracy together with recovery of bio signal semantics in the presence of sensor faults, while preserving the robustness of the watermark so that it is not easily corrupted, recovered or spoofed by malicious data consumers. Based on experimentally collected datasets from a gait-stability monitoring BASN, we show that our watermarking technique can robustly and effectively embed up to 1000 bit watermarks under these constraints.
机译：远程健康监测Basns通过在实时提供对诊断患者数据的访问来保护医疗保健质量的实质性改进。但是，通过故障危害数据诊断质量的威胁受到妨碍了采用。同时，通过自动化BASNS测量的敏感医疗数据的安全共享存在未解决的问题，从而源于提供数据所有者（BASN用户/患者）和数据消费者（医疗保险提供商，保险公司，医学研究设施）安全的需要根据共享控制医疗数据。我们通过强大的水印方法解决了这些问题，该问题被限制为留下初级数据语义度量，未受影响和次要度量的影响。此外，该方法与容错传感器分区技术协调，在存在传感器故障的情况下将高语义精度提供高度的语义精度，同时保持水印的鲁棒性，使其不易损坏，恢复或欺骗。通过恶意数据消费者。基于来自Gait-稳定监测BASN的实验收集的数据集，我们表明我们的水印技术可以鲁棒地和有效地嵌入这些约束下的1000位水印。

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