  Gait data captured by inertial sensors have demonstrated promising results on
user authentication. However, most existing approaches stored the enrolled gait
pattern insecurely for matching with the validating pattern, thus, posed
critical security and privacy issues. In this study, we present a gait
cryptosystem that generates from gait data the random key for user
authentication, meanwhile, secures the gait pattern. First, we propose a
revocable and random binary string extraction method using a deep neural
network followed by feature-wise binarization. A novel loss function for
network optimization is also designed, to tackle not only the intrauser
stability but also the inter-user randomness. Second, we propose a new
biometric key generation scheme, namely Irreversible Error Correct and
Obfuscate (IECO), improved from the Error Correct and Obfuscate (ECO) scheme,
to securely generate from the binary string the random and irreversible key.
The model was evaluated with two benchmark datasets as OU-ISIR and whuGAIT. We
showed that our model could generate the key of 139 bits from 5-second data
sequence with zero False Acceptance Rate (FAR) and False Rejection Rate (FRR)
smaller than 5.441%. In addition, the security and user privacy analyses showed
that our model was secure against existing attacks on biometric template
protection, and fulfilled irreversibility and unlinkability.
