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首页> 外文期刊>IEEE Journal of Solid-State Circuits >A Smart Hardware Security Engine Combining Entropy Sources of ECG, HRV, and SRAM PUF for Authentication and Secret Key Generation
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A Smart Hardware Security Engine Combining Entropy Sources of ECG, HRV, and SRAM PUF for Authentication and Secret Key Generation

机译：智能硬件安全引擎组合ECG，HRV和SRAM PUF的熵源进行身份验证和秘密密钥生成

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Securing personal data in wearable devices is becoming a crucial necessity as wearable devices are being deployed ubiquitously, which inadvertently exposes them to more sophisticated adversarial attacks. Although authentication systems using a single-entropy source, such as fingerprint or iris, are being used widely, successful spoofing attacks have been made, which show such systems' vulnerability. To mitigate these issues, new biometric modalities [e.g., electrocardiogram (ECG) and photoplethysmogram (PPG)], as well as multifactor authentication/security engine designs, are being investigated. In this work, we present a new smart hardware security engine that combines three different sources of entropy, ECG, heart rate variability (HRV), and SRAM-based physical unclonable function (PUF) to perform real-time authentication and generate unique/random signatures. Such hybrid signatures vary person-to-person, device-to-device, and over time, which significantly reduces the scope of an attack and enables secure personal device authentication as well as secret random key generation. The prototype chip fabricated in 65-nm LP CMOS consumes 4.04 mu W at 0.6 V for real-time authentication. Compared with ECG-only authentication, the average equal error rate of multi-source authentication is reduced by 7x down to 0.2375% for a 741-subject in-house ECG database. The generalization capability of the hardware was also tested by evaluating equal error rate (EER) values using other ECG databases available online. Also, 256-bit keys generated by optimally combining ECG, HRV, and PUF values fully pass nine NIST randomness tests.
机译：在可穿戴设备中保护个人数据正在成为关键的必要性，因为可穿戴设备正在普遍地部署，这无意中使其暴露于更复杂的对抗性攻击。虽然使用单熵源（例如指纹或虹膜）的认证系统被广泛使用，但已经成功地进行了成功的欺骗攻击，这表明了这种系统的漏洞。为了缓解这些问题，正在研究新的生物识别方式[例如，心电图（ECG）和光学仪（PPG）]以及多因素认证/安全引擎设计。在这项工作中，我们展示了一个新的智能硬件安全引擎，将三种不同的熵，心率，心率变异（HRV）和基于SRAM的物理不可渗透功能（PUF）结合起来执行实时认证并生成唯一/随机签名。这种混合签名可以改变人与人，设备到设备，随着时间的推移，这显着降低了攻击的范围，并实现了安全的个人设备认证以及秘密随机密钥生成。在65-NM LP CMOS中制造的原型芯片在0.6 V时消耗4.04μW以进行实时认证。与仅ECG身份验证相比，对于741个主题内部ECG数据库，多源认证的平均误差率为7x降至0.2375％。还通过使用在线可用的其他ECG数据库评估相同的错误率（eer）值来测试硬件的泛化能力。此外，通过最佳地组合ECG，HRV和PUF值来完全通过九个NIST随机性测试而产生的256位密钥。

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