Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT
Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT
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DOI：
10.1109/access.2019.2910753
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Forte, Domenic
中科院分区：
文献类型：
--
作者：
Karimian, Nima;Tehranipoor, Mark;Forte, Domenic
随着物联网的出现，对低功耗、普及设备的访问控制和数据保护的需求日益增长。基于密钥的生物识别密码系统由于其便利性和较低的攻击易感性而在物联网中很有前途。然而，与生物识别处理和模板保护相关的成本对于智能卡来说是不小的，等等。在本文中，我们讨论了成本与生物识别系统的效用，并研究了改进它们的框架。我们提出了噪声感知生物量化框架（NA-IOMBA），该框架能够以低注册时间和低成本生成唯一，可靠和高熵的密钥。首先，我们比较了其与IOMBA和一类支持向量机在多种生物识别模式上的性能，包括流行的生物识别模式（指纹和虹膜）和新兴的心血管模式（ECG和PPG）。结果表明，NA-IOMBA优于所有这些方法，ECG在可靠性、密钥长度、熵和实现成本之间提供了最好的权衡。其次，我们研究了在不同会话和不同心跳次数训练下获得的心电图对关键可靠性的影响。最后，实现结果表明，通过调整预处理、特征提取和后处理模块，将噪声模型与NA-IOMBA结合可以降低60%以上的功耗和利用率。
With the emergence of the Internet-of-Things, there is a growing need for access control and data protection on low-power, pervasive devices. Key-based biometric cryptosystems are promising for IoT due to its convenient nature and lower susceptibility to attacks. However, the costs associated with biometric processing and template protection are nontrivial for smart cards, and so forth. In this paper, we discuss the cost versus the utility of biometric systems and investigate frameworks for improving them. We propose the noise-aware biometric quantization framework (NA-IOMBA) capable of generating unique, reliable, and high entropy keys with low enrollment times and costs with several experiments. First, we compare its performance with IOMBA and one-class-SVM on multiple biometric modalities, including popular ones (fingerprint and iris) and emerging cardiovascular ones (ECG and PPG). The results show that NA-IOMBA outperforms them all and that ECG provides the best trade-off between reliability, key length, entropy, and implementation cost. Second, we examine the impact on key reliability with ECGs obtained at different sessions and trained with a different number of heartbeats. Finally, implementation results show that incorporating noise models with NA-IOMBA reduces power and utilization overhead by more than 60% by adapting the pre-processing, feature extraction, and post-processing modules.