Paper ID: 2305.16474

FairDP: Certified Fairness with Differential Privacy

Khang Tran, Ferdinando Fioretto, Issa Khalil, My T. Thai, NhatHai Phan

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.

Submitted: May 25, 2023