Synthetic data privacy evaluation

Privacy evaluation framework for synthetic data publishing

The framework implemented in this library measures the privacy gain of publishing a synthetic dataset in place of the raw data with respect to a specific privacy concern. Each concern is modelled as a privacy adversary that targets an individual record and aims to infer a secret about this record. The library includes implementations of two new privacy attacks on the output of a generative model. To evaluate privacy gain, the framework is instantiated under the chosen threat model and outputs an estimate about how much publishing the synthetic data instead of the raw data reduces the privacy loss of a chosen target record under this threat model.