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The closer the value is to 0 or 1, or the lower the number, the better

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BERTScore is a metric used to evaluate the semantic similarity between two texts, utilizing deep learning models like BERT (Bidirectional Encoder Representations from Transformers) to capture the meaning of the texts..

High BERTScore: If the value is 0.9 or higher, it indicates that the generated text is almost semantically identical to the real text. This can increase the risk of sensitive information leakage.
Low BERTScore: If the value is 0.6 or lower, it indicates lower similarity and a reduced risk of leakage.

Inference risk measures the risk of inferring sensitive information about the original data from synthetic data. It evaluates the likelihood of extracting original data information from synthetic data and is calculated by comparing the distance between synthetic and original data.

High Duplication Rate: Indicates lower data diversity and potential quality issues, which can reduce the reliability of analysis and models.
Interpretation: A lower risk value means that synthetic data is less likely to infer sensitive information, indicating higher data security.

Indistinguishability measures how well a machine learning model can differentiate between real and synthetic data. After the model has learned the patterns of real data, it is tested on mixed data to evaluate its accuracy.

High accuracy: Means the model effectively distinguishes between synthetic and real data.
Low accuracy close to 0.5: Indicates that the model frequently confuses synthetic data with real data, suggesting that synthetic data is quite similar to real data.

Downstream classification accuracy is an indicator used to evaluate the usefulness of synthetic data. It measures whether synthetic data performs similarly to real data. The method involves training the same model separately on real data and synthetic data, and then comparing the accuracies of the two models.

Interpretation: A high accuracy rate means that the model trained on synthetic data performs similarly to the one trained on real data, indicating that the synthetic data is of high quality and well represents the real data.

MMD (Maximum Mean Discrepancy) is a metric used to assess the similarity between two probability distributions. It is commonly used to compare generated data with real data.

High MMD score: A score above 0.05 indicates that the two distributions may differ.
Low MMD score: Indicates that the generated data is similar to the real data. A score close to 0 is preferable, and a score below 0.01 suggests that the two data distributions are nearly indistinguishable.