Generating Tabular Synthetic Data for Market Use: Balancing Privacy and Utility

a big magic box that provides a lot of synthetic data
We want to share our experience generating market-ready tabular synthetic data, a crucial tool for organizations navigating the ethical and legal landscape of data privacy.

The Need for Synthetic Data

Synthetic data offers a valuable solution for organizations seeking to leverage the power of data analytics while adhering to regulations like the GDPR (General Data Protection Regulation). It allows us to create realistic, yet anonymized datasets for various purposes like:

  • Healthcare: Analyze patient data for research and development without compromising patient confidentiality.
  • Finance: Develop and test financial models using synthetic customer data.
  • Research: Train machine learning algorithms on realistic synthetic data without privacy concerns.

We focused on generating tabular synthetic data, structured datasets commonly used in traditional data analysis.

Generation Techniques and Considerations

Several techniques exist for generating synthetic tabular data, each with its own advantages and limitations:

  • GaussianCopula: Efficient for low-dimensional data with simple relationships, but struggles with complex dependencies.
  • PrivBayes: Offers strong privacy guarantees, but may not accurately capture complex data distributions.
  • CTGAN: Handles high-dimensional data well, but requires careful parameter tuning for optimal results.
  • DPCTGAN: Combines Generative Adversarial Networks (GANs) with differential privacy for high-quality synthetic data, but can be computationally expensive.

Evaluating Synthetic Data Quality

Evaluating the quality of synthetic data ensures it retains the statistical properties and relationships of the original data while protecting privacy. Common evaluation methods include:

  • Statistical Comparison: Comparing key statistics (mean, standard deviation) between real and synthetic data.
  • Visual Inspection: Assessing the visual similarity between real and synthetic data distributions.
  • Machine Learning Performance: Training models on both real and synthetic data and comparing their performance on unseen data.

Impact of Data Size

Data size significantly impacts both generation and evaluation processes. Larger datasets generally require more advanced techniques like DPCTGAN and longer training times. Evaluation also becomes more complex with larger datasets, demanding powerful computing resources for statistical analysis and machine learning tasks.

Alternative Non-Tabular Forms of Synthetic Data

While we focused on tabular data, synthetic data can also be generated for other formats:

  • Images: Create realistic synthetic images for training computer vision algorithms.
  • Text: Generate anonymized yet grammatically correct text data for natural language processing tasks.
  • Time Series: Simulate temporal data patterns for forecasting and anomaly detection applications.

a machine of synthetic data


Generating high-quality tabular synthetic data requires careful consideration of various factors. Choosing the right generation technique and evaluation methods is crucial for creating synthetic data that is both statistically sound and protects privacy. By leveraging synthetic data, organizations can unlock the power of data analytics while complying with ethical and legal requirements.

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