Read the QA Report

Nearest neighbor distance ratio

This is the ratio of the first and the fifth nearest-neighbor distances of synthetic data points when measured against the target dataset. It allows you to compare inliers and outliers in the population on an equal base.

Synthetic data points with an NNDR close to 0 are near target points in sparse data regions, i.e., outlier target data points.

The test passes if the synthetic data points are not more similar to outliers in the target, compared to the target data points.

Identical match share

A measure of exact matches between synthetic and original data.

This metric counts the number of identical data points (copies) within the target data and compares it with the number of copies between the target dataset and the synthetic dataset.

The test passes if the number of copies in the synthetic dataset is less (or not significantly more) than within the target data itself.

Distance to closest record

A measure of the distances between synthetic records to their closest original records.

For each synthetic data point, this metric looks at the closest data point in the target dataset and compares that distribution of the closest distances to the observed distribution within the target data.

The test passes if, for the synthetic distribution of the closest records, low quantiles are not statistically below target data quantiles.

A threshold is defined for each quantile by the confidence interval generated via bootstrapping on the difference between target data and synthetic data set distribution.

Correlations

This tab shows three correlation matrices. They provide an easy way to assess whether the synthetic dataset retained the correlation structure of the original data set.

Both the X and Y-axis refer to the columns in your subject table, and each cell in the matrix correlates a variable pair: the more two variables are correlated, the darker the cell becomes. The third matrix shows the difference between the target and the synthetic data.

Univariate distributions

Univariate distributions describe the probability of a variable having a particular value. You can find four types of plots in this section of the QA report: categorical , continuous and datetime, but there’s also a Sequence Length plot if you synthesized a linked table.

For each variable, there’s a distribution and binned plot. These show the distributions of the original and the synthetic dataset in green and black, respectively. The percentage next to the title shows how accurately the original column is represented by the synthetic column.

You may find categories that are not present in the original dataset (for example, _RARE_). These categories appear as a means of rare category protection, ensuring privacy protection of subjects with rare or unique features.

Bivariate distributions

Bivariate distributions help you understand the conditional relationship between the content of two columns in your original dataset and how it changed in the synthetic dataset.

The bivariate distribution below shows, for instance, that the age group of forty years and older is most likely to be married, and anyone below thirty is most likely to have never been married. You can see that this is the same in the synthetic dataset.

If it’s a QA report for a linked table, then you can find the plots with the context table’s columns by looking for context:[column-name]. The word context here refers to either the subject table or another linked table with which this linked table has been synthesized.

You may find categories that are not present in the original dataset (for example, _RARE_). These categories appear as a means of rare category protection, ensuring privacy protection of subjects with rare or unique features.

Accuracy

The accuracy of synthetic data can be assessed by measuring statistical distances between the synthetic and the original data. The metric of choice for the statistical distance is the total variation distance (TVD), which is calculated for the discretized empirical distributions. Subtracting the TVD from 100% then yields the reported accuracy measure. These are being calculated for all univariate and all bivariate distributions. The latter is done for all pair-wise combinations within the target data, as well as between the context and the target. For sequential data, an additional coherence metric is calculated that assesses the bivariate accuracy between the value of a column, and the succeeding value of a column. All of these individual-level statistics are then averaged across to provide a single informative quantitative measure. The full list of calculated accuracies is provided as a separate downloadable file.