The DCRBaselineProtection Metric Crashes When The Distance Between Random Data And Real Data Is 0

Introduction

The DCRBaselineProtection metric is a crucial measure of privacy in synthetic data. It calculates the distance between synthetic data and real data, providing a score that indicates how private the synthetic data is. However, in certain cases, this metric crashes when the distance between random data and real data is 0. In this article, we will delve into the reasons behind this issue and propose a solution to prevent the metric from crashing.

Environment Details

Software and Hardware Details

• SDMetrics version: 0.19.1 (DCR Branch)
• Python version: Python 3.11
• Operating System: Linux Colab

Error Description

The DCRBaselineProtection metric is designed to measure the privacy of synthetic data by comparing it to random data. It calculates two key metrics:

• random_data_median: The typical distance between random data and real data
• synthetic_data_median: The typical distance between synthetic data and real data

The final score is calculated as: synthetic_data_median / random_data_median

However, in some cases, random_data_median=0. This occurs when the dataset has limited diversity, such as when there are only a few possible discrete values for each column.

Example Dataset

In this example, the dataset has only 2 columns, each with 2 possible discrete values, resulting in a total of 4 possibilities. When the random_data_median is 0, the metric crashes.

Expected Behavior

Instead of crashing, the final metric score should be NaN, indicating that it is not recommended to compute privacy on such a dataset. The compute_breakdown method should still return the individual median scores, allowing the user to understand more about what's happening.

Proposed Solution

To prevent the metric from crashing, we propose the following solution:

>>> DCRBaselineProtection.compute_breakdown(
  real_training_data=real_df,
  synthetic_data=synthetic_df,
  metadata=my_metadata)
{
  'score': NaN,
  'median_DCR_to_real_data': {
    'synthetic_data': 0.25
    'random_data_baseline': 0.0
  }
}

In this solution, when random_data_median=0, the final metric score is set to NaN, and the compute_breakdown method returns the individual median scores.

Implementation

To implement this solution, we need to modify the DCRBaselineProtection metric to handle the case where random_data_median=0. We can do this by adding a simple check in the metric calculation:

def compute_breakdown(
  self,
  real_training_data,
  synthetic_data,
  metadata
):
  # Calculate random_data_median and synthetic_data_median
  random_data_median = self.calculate_median_distance(
    real_training_data, synthetic_data
  )
  synthetic_data_median = self.calculate_median_distance(
    real_training_data, synthetic_data
  )

  # Check if random_data_median is 0
  if random_data_median == 0:
    # Set final metric score to NaN
    score = float('nan')
  else:
    # Calculate final metric score
    score = synthetic_data_median / random_data_median

  # Return individual median scores
  return {
    'score': score,
    'median_DCR_to_real_data': {
      'synthetic_data': synthetic_data_median,
      'random_data_baseline': random_data_median
    }
  }

Conclusion

Q: What is the DCRBaselineProtection metric?

A: The DCRBaselineProtection metric is a measure of privacy in synthetic data. It calculates the distance between synthetic data and real data, providing a score that indicates how private the synthetic data is.

Q: Why does the DCRBaselineProtection metric crash when the distance between random data and real data is 0?

A: The metric crashes when the distance between random data and real data is 0 because it is designed to calculate a score by dividing the distance between synthetic data and real data by the distance between random data and real data. When the distance between random data and real data is 0, the metric cannot calculate a valid score, resulting in a crash.

Q: What is the expected behavior when the distance between random data and real data is 0?

A: The expected behavior is for the final metric score to be NaN, indicating that it is not recommended to compute privacy on such a dataset. The compute_breakdown method should still return the individual median scores, allowing the user to understand more about what's happening.

Q: How can I prevent the DCRBaselineProtection metric from crashing?

A: To prevent the metric from crashing, you can modify the DCRBaselineProtection metric to handle the case where the distance between random data and real data is 0. This can be done by adding a simple check in the metric calculation to set the final metric score to NaN when the distance between random data and real data is 0.

Q: What is the proposed solution to prevent the DCRBaselineProtection metric from crashing?

A: The proposed solution is to modify the DCRBaselineProtection metric to handle the case where the distance between random data and real data is 0. This can be done by adding a simple check in the metric calculation to set the final metric score to NaN when the distance between random data and real data is 0.

Q: How can I implement the proposed solution?

A: To implement the proposed solution, you can modify the DCRBaselineProtection metric to handle the case where the distance between random data and real data is 0. This can be done by adding a simple check in the metric calculation to set the final metric score to NaN when the distance between random data and real data is 0.

Q: What are the benefits of implementing the proposed solution?

A: The benefits of implementing the proposed solution are:

• The metric will no longer crash when the distance between random data and real data is 0.
• The final metric score will be accurate and reliable, even in cases where the dataset has limited diversity.
• The compute_breakdown method will still return the individual median scores, allowing the user to understand more about what's happening.

Q: Are there any potential drawbacks to implementing the proposed solution?

A: There are no potential drawbacks to implementing the proposed solution. The solution is designed to handle the case where the distance between random data and real data is 0, and it will not affect the accuracy or reliability of the metric in any way.

Q: How can I get started with implementing the proposed solution?

A: To get started with implementing the proposed solution, you can follow these steps:

1. Modify the DCRBaselineProtection metric to handle the case where the distance between random data and real data is 0.
2. Add a simple check in the metric calculation to set the final metric score to NaN when the distance between random data and real data is 0.
3. Test the modified metric to ensure that it is working correctly and accurately.

By following these steps, you can implement the proposed solution and prevent the DCRBaselineProtection metric from crashing when the distance between random data and real data is 0.