Builder

Classification details for data columns that specify how each column should be treated during synthetic data generation.

The epsilon value for differential privacy, which controls the privacy-utility tradeoff in synthetic data generation. Lower values provide stronger privacy guarantees but may reduce data utility.

The maximum acceptable score for membership inference attack vulnerability. Synthetic data generation fails if the score for the resulting data exceeds this threshold.

construct an aws.sdk.kotlin.services.cleanroomsml.model.ColumnClassificationDetails inside the given block