Auto-Key: Using Autoencoder to Speed Up Gait-based Key Generation in Body Area Networks

With the rising popularity of wearable devices and sensors, shielding Body Area Networks (BANs) from eavesdroppers has become an urgent problem to solve. Since the conventional key distribution systems are too onerous for resource-constrained wearable sensors, researchers are pursuing a new light-weight key generation approach that enables two wearable devices attached at different locations of the user body to generate an identical key simultaneously simply from their independent observations of user gait. A key challenge for such gait-based key generation lies in matching the bits of the keys generated by independent devices despite the noisy sensor measurements, especially when the devices are located far apart on the body affected by different sources of noise. To address the challenge, we propose a novel machine learning framework, called Auto-Key, that uses an autoencoder to help one device predict the gait observations at another distant device attached to the same body and generate the key using the predicted sensor data. We prototype the proposed method and evaluate it using a public acceleration dataset collected from 15 real subjects wearing accelerometers attached to seven different locations of the body. Our results show that, on average, Auto-Key increases the matching rate of independently generated bits from two sensors attached at two different locations by 16.5%, which speeds up the successful generation of fully-matching symmetric keys at independent wearable sensors by a factor of 1.9. In the proposed framework, a subject-specific model can be trained with 50% fewer data and 88% less time by retraining a pre-trained general model when compared to training a new model from scratch. The reduced training complexity makes Auto-Key more practical for edge computing, which provides better privacy protection to biometric and behavioral data compared to cloud-based training.