  With the widespread adoption of the quantified self movement, an increasing
number of users rely on mobile applications to monitor their physical activity
through their smartphones. Granting to applications a direct access to sensor
data expose users to privacy risks. Indeed, usually these motion sensor data
are transmitted to analytics applications hosted on the cloud leveraging
machine learning models to provide feedback on their health to users. However,
nothing prevents the service provider to infer private and sensitive
information about a user such as health or demographic attributes.In this
paper, we present DySan, a privacy-preserving framework to sanitize motion
sensor data against unwanted sensitive inferences (i.e., improving privacy)
while limiting the loss of accuracy on the physical activity monitoring (i.e.,
maintaining data utility). To ensure a good trade-off between utility and
privacy, DySan leverages on the framework of Generative Adversarial Network
(GAN) to sanitize the sensor data. More precisely, by learning in a competitive
manner several networks, DySan is able to build models that sanitize motion
data against inferences on a specified sensitive attribute (e.g., gender) while
maintaining a high accuracy on activity recognition. In addition, DySan
dynamically selects the sanitizing model which maximize the privacy according
to the incoming data. Experiments conducted on real datasets demonstrate that
DySan can drasticallylimit the gender inference to 47% while only reducing the
accuracy of activity recognition by 3%.
