Fairness-Aware Differential Privacy: A Fairly Proportional Noise Mechanism

Summary

The paper proposes the Fairly Proportional Noise Mechanism (FPNM), a differential privacy method that accounts for both the direction and magnitude of noise relative to raw query results to reduce unfairness across groups. In decision tasks, FPNM reduces unfairness by average reductions of 19.17% in F-Norm and 17.32% in average unfairness. When combined with DP-SGD for learning tasks, it achieves fairness comparable to fairness-aware baselines with better accuracy, while empirical privacy holds under membership inference attacks.