A Unified Framework for Quantifying Privacy Risk in Synthetic Data. (arXiv:2211.10459v1 [cs.CR])

Synthetic data is often presented as a method for sharing sensitive
information in a privacy-preserving manner by reproducing the global
statistical properties of the original data without disclosing sensitive
information about any individual. In practice, as with other anonymization
methods, privacy risks cannot be entirely eliminated. The residual privacy
risks need instead to be ex-post assessed. We present Anonymeter, a statistical
framework to jointly quantify different types of privacy risks in synthetic
tabular datasets. We equip this framework with attack-based …

data framework privacy risk synthetic data