  Motion sensors such as accelerometers and gyroscopes measure the instant
acceleration and rotation of a device, in three dimensions. Raw data streams
from motion sensors embedded in portable and wearable devices may reveal
private information about users without their awareness. For example, motion
data might disclose the weight or gender of a user, or enable their
re-identification. To address this problem, we propose an on-device
transformation of sensor data to be shared for specific applications, such as
monitoring selected daily activities, without revealing information that
enables user identification. We formulate the anonymization problem using an
information-theoretic approach and propose a new multi-objective loss function
for training deep autoencoders. This loss function helps minimizing
user-identity information as well as data distortion to preserve the
application-specific utility. The training process regulates the encoder to
disregard user-identifiable patterns and tunes the decoder to shape the output
independently of users in the training set. The trained autoencoder can be
deployed on a mobile or wearable device to anonymize sensor data even for users
who are not included in the training dataset. Data from 24 users transformed by
the proposed anonymizing autoencoder lead to a promising trade-off between
utility and privacy, with an accuracy for activity recognition above 92% and an
accuracy for user identification below 7%.
