  Deployment of deep learning in different fields and industries is growing day
by day due to its performance, which relies on the availability of data and
compute. Data is often crowd-sourced and contains sensitive information about
its contributors, which leaks into models that are trained on it. To achieve
rigorous privacy guarantees, differentially private training mechanisms are
used. However, it has recently been shown that differential privacy can
exacerbate existing biases in the data and have disparate impacts on the
accuracy of different subgroups of data. In this paper, we aim to study these
effects within differentially private deep learning. Specifically, we aim to
study how different levels of imbalance in the data affect the accuracy and the
fairness of the decisions made by the model, given different levels of privacy.
We demonstrate that even small imbalances and loose privacy guarantees can
cause disparate impacts.
