Overview

We assess the quality of synthetic data by examining its fidelity, privacy, and downstream utility.

Fidelity Evaluation

To effectively utilize the generated data for your specific use case, it is crucial to ensure that the synthetic data accurately reflects the statistical characteristics of the real data. This concept is referred to as synthetic data fidelity evaluation.

See details in Fidelity Metrics and Visualization.

Privacy Evaluation

Evaluating privacy metrics is critical because it ensures that the synthetic data closely resembles real data wihtout exposing sensitive individual records. By assessing the risk of re-identification and ensuring the synthetic data maintains privacy while remaining useful, organizations can confidently share and use data without violating privacy regulations or compromising personal information.

See details in Privacy Metrics.

Downstream Utility Evaluation

Downstream utility evaluation is employed to determine how effectively synthetic data can serve as a substitute for real data in practical applications. This evaluation is typically conducted on both real and synthetic datasets, with the quality of the synthetic data being assessed by the narrowness of the performance gap between them.

See details in Downstream Utility Metrics.