  Recent advances in differentially private deep learning have demonstrated
that application of differential privacy, specifically the DP-SGD algorithm,
has a disparate impact on different sub-groups in the population, which leads
to a significantly high drop-in model utility for sub-populations that are
under-represented (minorities), compared to well-represented ones. In this
work, we aim to compare PATE, another mechanism for training deep learning
models using differential privacy, with DP-SGD in terms of fairness. We show
that PATE does have a disparate impact too, however, it is much less severe
than DP-SGD. We draw insights from this observation on what might be promising
directions in achieving better fairness-privacy trade-offs.
