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Assessment of differentially private synthetic data for utility and fairness in end-to-end machine learning pipelines for tabular data
Pereira M,
Kshirsagar M,
Mukherjee S,
Dodhia R,
Lavista Ferres J,
et al.
(2024)
Assessment of differentially private synthetic data for utility and fairness in end-to-end machine learning pipelines for tabular data.
PLOS ONE 19(2): e0297271.
https://doi.org/10.1371/journal.pone.0297271