Data Synthesis Generation

Data Synthesis and Generation

Generating synthetic data for ML training — from tabular data synthesis through image generation with GANs/diffusion models and privacy-preserving synthetic data.

When to Use

• Insufficient real data for ML training
• Privacy concerns with real data (HIPAA, GDPR)
• Balancing class distributions (rare events, edge cases)
• Testing systems with controlled data properties

Synthesis Methods

SYNTHESIS_METHODS = {
    'tabular': 'CTGAN, TVAE, SDV — preserve statistical properties of real data',
    'image': 'Stable Diffusion, StyleGAN, DALL-E — photorealistic or domain-specific',
    'text': 'LLM-based generation — prompts to generate labeled text data',
    'time_series': 'DoppelGANger, TimeGAN — temporal pattern preservation',
    'privacy': 'Differentially private synthetic data — mathematical privacy guarantee',
}

def generate_tabular_synthetic(real_data: pd.DataFrame, n_rows: int = 1000) -> pd.DataFrame:
    """Generate synthetic tabular data using SDV."""
    from sdv.tabular import CTGAN
    model = CTGAN(epochs=50)
    model.fit(real_data)
    return model.sample(n_rows)

Verification Checklist

• Synthetic data preserves key statistical properties (means, correlations, distributions)
• No identity leakage from training data (privacy audit)
• Downstream model performance matches real-data training
• Edge cases and rare events adequately represented
• Synthetic data quality metrics measured (statistical similarity, utility)
• Privacy guarantees documented (differential privacy epsilon if applicable)