Data from motion sensors such as accelerometers and gyroscopes embedded in
our devices can reveal secondary undesired, private information about our
activities. This information can be used for malicious purposes such as user
identification by application developers. To address this problem, we propose a
data transformation mechanism that enables a device to share data for specific
applications (e.g.~monitoring their daily activities) without revealing private
user information (e.g.~ user identity). We formulate this anonymization process
based on an information theoretic approach and propose a new multi-objective
loss function for training convolutional auto-encoders~(CAEs) to provide a
practical approximation to our anonymization problem. This effective loss
function forces the transformed data to minimize the information about the
user's identity, as well as the data distortion to preserve
application-specific utility. Our training process regulates the encoder to
disregard user-identifiable patterns and tunes the decoder to shape the final
output independently of users in the training set. Then, a trained CAE can be
deployed on a user's mobile device to anonymize sensor data before sharing with
an app, even for users who are not included in the training dataset. The
results, on a dataset of 24 users for activity recognition, show a promising
trade-off on transformed data between utility and privacy, with an accuracy for
activity recognition over 92%, while reducing the chance of identifying a user
to less than 7%.