  Deep neural networks have become a primary tool for solving problems in many
fields. They are also used for addressing information retrieval problems and
show strong performance in several tasks. Training these models requires large,
representative datasets and for most IR tasks, such data contains sensitive
information from users. Privacy and confidentiality concerns prevent many data
owners from sharing the data, thus today the research community can only
benefit from research on large-scale datasets in a limited manner. In this
paper, we discuss privacy preserving mimic learning, i.e., using predictions
from a privacy preserving trained model instead of labels from the original
sensitive training data as a supervision signal. We present the results of
preliminary experiments in which we apply the idea of mimic learning and
privacy preserving mimic learning for the task of document re-ranking as one of
the core IR tasks. This research is a step toward laying the ground for
enabling researchers from data-rich environments to share knowledge learned
from actual users' data, which should facilitate research collaborations.
