Vin'icius Gabriel Angelozzi

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data

It is demonstrated that while DP alone can degrade both utility and fairness, applying fairness interventions can partially restore equitable outcomes, and post-processing methods tend to provide more stable fairness–utility trade-offs across privacy budgets and synthesizers.