  Statistical methods protecting sensitive information or the identity of the
data owner have become critical to ensure privacy of individuals as well as of
organizations. This paper investigates anonymization methods based on
representation learning and deep neural networks, and motivated by novel
information theoretical bounds. We introduce a novel training objective for
simultaneously training a predictor over target variables of interest (the
regular labels) while preventing an intermediate representation to be
predictive of the private labels. The architecture is based on three
sub-networks: one going from input to representation, one from representation
to predicted regular labels, and one from representation to predicted private
labels. The training procedure aims at learning representations that preserve
the relevant part of the information (about regular labels) while dismissing
information about the private labels which correspond to the identity of a
person. We demonstrate the success of this approach for two distinct
classification versus anonymization tasks (handwritten digits and sentiment
analysis).
