  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.
