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