  This paper introduces FairDP, a novel mechanism designed to achieve certified
fairness with differential privacy (DP). FairDP independently trains models for
distinct individual groups, using group-specific clipping terms to assess and
bound the disparate impacts of DP. Throughout the training process, the
mechanism progressively integrates knowledge from group models to formulate a
comprehensive model that balances privacy, utility, and fairness in downstream
tasks. Extensive theoretical and empirical analyses validate the efficacy of
FairDP and improved trade-offs between model utility, privacy, and fairness
compared with existing methods.
