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 μW 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 7× 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.

中文翻译：

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智能硬件安全引擎结合 ECG、HRV 和 SRAM PUF 的熵源进行身份验证和密钥生成


随着可穿戴设备的广泛部署，保护可穿戴设备中的个人数据变得至关重要，这无意中使它们面临更复杂的对抗性攻击。尽管使用单熵源（例如指纹或虹膜）的身份验证系统正在广泛使用，但已经进行了成功的欺骗攻击，这表明此类系统的脆弱性。为了缓解这些问题，正在研究新的生物识别模式[例如心电图（ECG）和光电体积描记图（PPG）]以及多因素身份验证/安全引擎设计。在这项工作中，我们提出了一种新的智能硬件安全引擎，它结合了三种不同的熵源、心电图、心率变异性 (HRV) 和基于 SRAM 的物理不可克隆函数 (PUF)，以执行实时身份验证并生成唯一/随机的数据签名。这种混合签名会随着时间的推移而因人而异、因设备而异，从而显着缩小攻击范围，并实现安全的个人设备身份验证以及秘密随机密钥生成。采用 65 nm LP CMOS 制造的原型芯片在 0.6 V 电压下的功耗为 4.04 μW，用于实时身份验证。与仅心电图认证相比，对于741个受试者的内部心电图数据库，多源认证的平均等错误率降低了7倍，达到0.2375%。还通过使用其他在线可用的心电图数据库评估等错误率 (EER) 值来测试硬件的泛化能力。此外，通过最佳组合 ECG、HRV 和 PUF 值生成的 256 位密钥完全通过了九项 NIST 随机性测试。