smartnoise-eval 0.3.1

Evaluation of differentially private tabular data

SmartNoise Evaluator

The SmartNoise Evaluator is designed to help assess the privacy and accuracy of differentially private queries. It includes:

• Analyze: Analyze a dataset and provide information about cardinality, data types, independencies, and other information that is useful for creating a privacy pipeline
• Evaluate: Compares the privatized results to the true results and provides information about the accuracy and bias

These tools currently require PySpark.

Analyze

Analyze provides metrics about a single dataset.

• Percent of all dimension combinations that are unique, k < 5 and k < 10 (Count up to configurable “reporting length”)
• Report which columns are “most linkable”
• Marginal histograms up to n-way -- choose default with reasonable size (e.g. 10 per marginal, and up to 20 marginals -- allow override). Trim and encode labels.
• Number of rows
• Number of distinct rows
• Count, Mean, Variance, Min, Max, Median, Percentiles for each marginal
• Classification AUC
• Individual Cardinalities
• Dimensionality, Sparsity
• Independencies

Evaluate

Evaluate compares an original data file with one or more comparison files. It can compare any of the single-file metrics computed in Analyze as well as a number of metrics that involve two datasets. When more than one comparison dataset is provided, we can provide all of the two-way comparisons with the original, and allow the consumer to combine these measures (e.g. average over all datasets)

• How many dimension combinations are suppressed
• How many dimension combinations are fabricated
• How many redacted rows (fully redacted vs. partly redacted)
• Mean error in the count across categories by 1-way, 2-way, etc.
• Mean absolute error by 1-way, 2-way, etc. up to reporting length
  • Also do for user specified dimension combinations
  • Report by bin size (e.g., < 1000, >= 1000)
• Mean proportional error by 1-way, 2-way, etc.