  Numerous studies in the literature have already shown the potential of
biometrics on mobile devices for authentication purposes. However, it has been
shown that, the learning processes associated to biometric systems might expose
sensitive personal information about the subjects. This study proposes
GaitPrivacyON, a novel mobile gait biometrics verification approach that
provides accurate authentication results while preserving the sensitive
information of the subject. It comprises two modules: i) a convolutional
Autoencoder that transforms attributes of the biometric raw data, such as the
gender or the activity being performed, into a new privacy-preserving
representation; and ii) a mobile gait verification system based on the
combination of Convolutional Neural Networks (CNNs) and Recurrent Neural
Networks (RNNs) with a Siamese architecture. The main advantage of
GaitPrivacyON is that the first module (convolutional Autoencoder) is trained
in an unsupervised way, without specifying the sensitive attributes of the
subject to protect. The experimental results achieved using two popular
databases (MotionSense and MobiAct) suggest the potential of GaitPrivacyON to
significantly improve the privacy of the subject while keeping user
authentication results higher than 99% Area Under the Curve (AUC). To the best
of our knowledge, this is the first mobile gait verification approach that
considers privacy-preserving methods trained in an unsupervised way.
