Smart wearable devices (SWDs) collect and store sensitive daily information of many people. Its primary method of identification is still the password unlocking method. However, several studies have shown serious security flaws in that method, which makes the privacy and security concerns of SWDs particularly urgent. Gait identification is well suited for SWDs because its built-in sensors can provide data support for identification. However, existing gait identification methods have low accuracy and neglect to protect the privacy of gait features. In addition, the SWD can be used as an internet of things device for users to share data. But few studies have used gait feature-based encryption schemes to protect the privacy of message interactions between SWDs and other devices. In this paper, we propose a gait identification network, a bi-directional long short-term memory network with an attention mechanism (ABLSTM), to improve the identification accuracy and a stochastic orthogonal transformation (SOT) scheme to protect the extracted gait features from leakage. In the experiments, ABLSTM achieves an accuracy of 95.28%, reducing previous error rate by 19.3%. The SOT scheme is proved to be resistant to the chosen plaintext attack (CPA) and is 30% faster than previous methods. A biometric-based encryption scheme is proposed to enable secure message interactions using gait features as keys after the gait identification stage is passed, and offers better protection of the gait features compared to previous schemes.

翻译：智能穿戴设备收集并存储大量用户的敏感日常信息，其当前主要身份认证方式仍为密码解锁。然而研究表明，该方法存在严重安全缺陷，使智能穿戴设备的隐私安全问题尤为突出。步态识别因其内置传感器可提供识别数据支持而适用于智能穿戴设备，但现有步态识别方法准确率较低且忽视步态特征隐私保护。此外，智能穿戴设备可作为物联网设备供用户共享数据，但鲜有研究采用基于步态特征的加密方案保护设备间的消息交互隐私。本文提出结合注意力机制的双向长短期记忆网络（ABLSTM）步态识别模型以提高识别准确率，并设计随机正交变换（SOT）方案防止提取的步态特征泄露。实验中，ABLSTM识别准确率达95.28%，较现有方法错误率降低19.3%；SOT方案被证明可抵抗选择明文攻击（CPA），且运算速度较现有方法提升30%。本文还提出基于生物特征的加密方案，在完成步态识别阶段后以步态特征为密钥实现安全消息交互，相比现有方案能更有效地保护步态特征。