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Evaluating the Fairness Impact of Differentially Private Synthetic Data
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Differentially private (DP) synthetic data is a promising approach to maximizing the utility of data containing sensitive information. Due to the suppression of underrepresented classes that is often required to achieve privacy, however, it may be in conflict with fairness. We evaluate four DP synthesizers and present empirical results indicating that three of these models frequently degrade fairness outcomes on downstream binary classification tasks. We draw a connection between fairness and the proportion of minority groups present in the generated synthetic data, and find that training synthesizers on data that are pre-processed via a multi-label undersampling method can promote more fair outcomes without degrading accuracy.
Forward citations
Cited by 2 Pith papers
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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data
Post-processing fairness interventions (ROC, EqOdds) provide the most stable fairness-utility trade-offs when training on differentially private synthetic tabular data, partially recovering DP-induced fairness degradation.
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Disparate Impact in Synthetic Data Generation
Synthetic data generation exhibits disparate impact from group-specific approximation, sampling, and estimation errors; group-wise models improve both utility and parity on graphical model methods.
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