  When we enforce differential privacy in machine learning, the utility-privacy
trade-off is different w.r.t. each group. Gradient clipping and random noise
addition disproportionately affect underrepresented and complex classes and
subgroups, which results in inequality in utility loss. In this work, we
analyze the inequality in utility loss by differential privacy and propose a
modified differentially private stochastic gradient descent (DPSGD), called
DPSGD-F, to remove the potential disparate impact of differential privacy on
the protected group. DPSGD-F adjusts the contribution of samples in a group
depending on the group clipping bias such that differential privacy has no
disparate impact on group utility. Our experimental evaluation shows how group
sample size and group clipping bias affect the impact of differential privacy
in DPSGD, and how adaptive clipping for each group helps to mitigate the
disparate impact caused by differential privacy in DPSGD-F.
