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Really Useful Synthetic Data – A Framework to Evaluate the Quality of Differentially Private Synthetic Data
[article]
2021
arXiv
pre-print
Recent advances in generating synthetic data that allow to add principled ways of protecting privacy -- such as Differential Privacy -- are a crucial step in sharing statistical information in a privacy preserving way. But while the focus has been on privacy guarantees, the resulting private synthetic data is only useful if it still carries statistical information from the original data. To further optimise the inherent trade-off between data privacy and data quality, it is necessary to think
arXiv:2004.07740v2
fatcat:ar35f4jc75frnbwax3pwgzi32u