  The rapid rise of IoT and Big Data has facilitated copious data driven
applications to enhance our quality of life. However, the omnipresent and
all-encompassing nature of the data collection can generate privacy concerns.
Hence, there is a strong need to develop techniques that ensure the data serve
only the intended purposes, giving users control over the information they
share. To this end, this paper studies new variants of supervised and
adversarial learning methods, which remove the sensitive information in the
data before they are sent out for a particular application. The explored
methods optimize privacy preserving feature mappings and predictive models
simultaneously in an end-to-end fashion. Additionally, the models are built
with an emphasis on placing little computational burden on the user side so
that the data can be desensitized on device in a cheap manner. Experimental
results on mobile sensing and face datasets demonstrate that our models can
successfully maintain the utility performances of predictive models while
causing sensitive predictions to perform poorly.
