There is growing concern about how personal data are used when users grant applications direct access to the sensors in their mobile devices. For example,
time-series data generated by motion sensors reflect directly users' activities
and indirectly their personalities. It is therefore important to design
privacy-preserving data analysis methods that can run on mobile devices. In
this paper, we propose a feature learning architecture that can be deployed in
distributed environments to provide flexible and negotiable privacy-preserving
data transmission. It should be flexible because the internal architecture of
each component can be independently changed according to users or service
providers needs. It is negotiable because expected privacy and utility can be
negotiated based on the requirements of the data subject and underlying
application. For the specific use-case of activity recognition, we conducted
experiments on two real-world datasets of smartphone's motion sensors, one of
them is collected by the authors and will be publicly available by this paper
for the first time. Results indicate the proposed framework establishes a good
trade-off between application's utility and data subjects' privacy. We show
that it maintains the usefulness of the transformed data for activity
recognition (with around an average loss of three percentage points) while
almost eliminating the possibility of gender classification (from more than
90\% to around 50\%, the target random guess). These results also have
implication for moving from the current binary setting of granting permission
to mobile apps or not, toward a situation where users can grant each
application permission over a limited range of inferences according to the
provided services.