  The use of mutual information as a tool in private data sharing has remained
an open challenge due to the difficulty of its estimation in practice. In this
paper, we propose InfoShape, a task-based encoder that aims to remove
unnecessary sensitive information from training data while maintaining enough
relevant information for a particular ML training task. We achieve this goal by
utilizing mutual information estimators that are based on neural networks, in
order to measure two performance metrics, privacy and utility. Using these
together in a Lagrangian optimization, we train a separate neural network as a
lossy encoder. We empirically show that InfoShape is capable of shaping the
encoded samples to be informative for a specific downstream task while
eliminating unnecessary sensitive information. Moreover, we demonstrate that
the classification accuracy of downstream models has a meaningful connection
with our utility and privacy measures.
