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Found 3 papers, 1 papers with code
Evaluating the Fairness Impact of Differentially Private Synthetic Data
Differentially private (DP) synthetic data is a promising approach to maximizing the utility of data containing sensitive information.
Spending Privacy Budget Fairly and Wisely
Our methods are based on the insights that feature importance can inform how privacy budget is allocated, and, further, that per-group feature importance and fairness-related performance objectives can be incorporated in the allocation.
Differentially Private Synthetic Data: Applied Evaluations and Enhancements
Differentially private data synthesis protects personal details from exposure, and allows for the training of differentially private machine learning models on privately generated datasets.
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