Practical Privacy-Preserving ECG-Based Authentication for IoT-Based Healthcare
Practical Privacy-Preserving ECG-Based Authentication for IoT-Based Healthcare
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DOI：
10.1109/jiot.2019.2929087
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发表时间：
2019-10-01
影响因子：
10.6
通讯作者：
Fang, Yuguang
中科院分区：
文献类型：
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作者：
Huang, Pei;Guo, Linke;Fang, Yuguang
在当前的医疗保健系统中，患者使用各种类型的医疗物联网设备来监测他们的健康状况。收集的信息（个人健康记录）将被送回医院进行诊断和快速反应。然而，由于所监控的健康数据包含敏感信息，因此在数据隐私和身份认证方面会发生严重的安全和隐私泄漏。因此，数据应该得到很好的保护，免受未经授权的实体的攻击。然而，传统的密码学方法或基于密码的机制由于效率低和基于知识的特性而不能满足健康监测中的隐私和安全需求。生物特征认证克服了这些缺陷，并成功地验证了人类的固有特征。在所有的生物特征中，心电图（ECG）信号由于其医学特性而成为最合适的一种。然而，基于ECG的认证的安全性和隐私性目标通常在实践中失败，由于在收集的ECG数据中的噪声干扰和ECG数据库的隐私泄露。在本文中，我们提出了一个实用的方案，可以可靠地验证患者与噪声的ECG信号，并提供差分隐私保护的同时。我们的计划的有效性和效率进行了深入的分析和评估在线数据集。我们还对经历不同运动水平的人类受试者进行了一项试点研究，以验证我们的计划。
In current healthcare systems, patients use various types of medical Internet of Things devices for monitoring their health conditions. The collected information (personal health records) will be sent back to hospitals for diagnosis and quick responses. However, severe security and privacy leakages with regard to data privacy and identity authentication are incurred because the monitored health data contains sensitive information. Therefore, the data should be well protected from unauthorized entities. Unfortunately, traditional cryptographic approaches or password-based mechanisms cannot fulfill the privacy and security demands in health monitoring due to their low efficiency and knowledge-based property. Biometric authentication overcomes these deficiencies and successfully verifies the inherent characteristics of humans. Among all biometrics, the electrocardiogram (ECG) signal is the most suitable one due to its medical properties. However, the security and privacy objectives of ECG-based authentication usually fail in practice due to the noise interferences in the collected ECG data and the privacy breach of the ECG database. In this paper, we propose a practical scheme that can reliably authenticate patients with noisy ECG signals and provide differentially private protection simultaneously. The effectiveness and efficiency of our scheme are thoroughly analyzed and evaluated over online datasets. We also conduct a pilot study on human subjects experiencing different exercise levels to validate our scheme.