  It is becoming increasingly clear that users should own and control their
data. Utility providers are also becoming more interested in guaranteeing data
privacy. As such, users and utility providers should collaborate in data
privacy, a paradigm that has not yet been developed in the privacy research
community. We introduce this concept and present explicit architectures where
the user controls what characteristics of the data she/he wants to share and
what she/he wants to keep private. This is achieved by collaborative learning a
sensitization function, either a deterministic or a stochastic one, that
retains valuable information for the utility tasks but it also eliminates
necessary information for the privacy ones. As illustration examples, we
implement them using a plug-and-play approach, where no algorithm is changed at
the system provider end, and an adversarial approach, where minor re-training
of the privacy inferring engine is allowed. In both cases the learned
sanitization function keeps the data in the original domain, thereby allowing
the system to use the same algorithms it was using before for both original and
privatized data. We show how we can maintain utility while fully protecting
private information if the user chooses to do so, even when the first is harder
than the second, as in the case here illustrated of identity detection while
hiding gender.
