How to Generate Synthetic Data with LLMs While Maintaining Differential Privacy

Why differential privacy is crucial in the era of generative AI

Today’s businesses need high-quality training data, but they also must comply with strict regulations like GDPR and protect user data. Differential privacy offers a mathematically proven way to share useful information without exposing personal data. How does it work with large language models (LLMs) and synthetic data generation? Let’s explore this step by step.

The current context: AI that creates content

By 2026, LLMs have become essential to many workflows:

• Analytics teams use models to generate reports, code, and marketing content.
• Products like JetBrains’ KotlinLLM offer smart macros that generate Kotlin code in real time.
• Platforms like PolyAI use voice-native dialog models for realistic customer interactions.

All these use cases require realistic data, but often lack privacy-ready real datasets. That’s where synthetic data comes in.

How to generate synthetic data with LLMs

Generating synthetic data means using an LLM to create examples that reflect real-world distributions while maintaining the structural characteristics needed for training or testing. Here’s a simple, production-ready workflow.

Step 1: Design a prompt that captures domain logic

A well-structured prompt is the foundation for high-quality synthesis. For a financial transaction dataset, for example:

# Prompt for an LLM
# Goal: Generate synthetic financial transaction data rows.
# Schema: {transaction_id, user_id, amount, currency, date, category}
# Rule: Ensure amount is positive and category is one of the allowed options.

Generate 100 rows of financial transaction data following the above schema, respecting the following constraints:
- user_id must be a valid UUID.
- amount must be a decimal number with a maximum of 2 decimal places.
- currency must be one of the following: USD, EUR, GBP, JPY.
- date must be a timestamp in ISO 8601 format.
- category must be one of the following: "consumer goods", "entertainment", "travel", "subscriptions", "health".

Step 2: Run the model and clean the output

Once the raw text is generated, clean it into CSV or JSON. Use tools like OpenAI or open-source endpoints (e.g., LLaMA 2) via API. Here’s a basic cleaning example in Python:

import json
import pandas as pd

def clean_synthetic_data(raw_text):
    # Extract JSON blocks
    lines = raw_text.strip().split('\n')
    records = []
    for line in lines:
        if line.startswith('{') and line.endswith('}'):
            records.append(json.loads(line))
    df = pd.DataFrame(records)
    # Convert data types
    df['transaction_id'] = df['transaction_id'].astype(str)
    df['user_id'] = df['user_id'].astype(str)
    df['amount'] = pd.to_numeric(df['amount'], errors='coerce')
    df['date'] = pd.to_datetime(df['date'], errors='coerce')
    return df

Step 3: Verify the quality of synthetic data

Perform statistical checks: distributions of means, variances, and missing values. Compare with real data using scatter plots or histograms to ensure synthetic data captures real-world trends.

Integrating differential privacy into synthesis

Combining synthetic data generation with differential privacy adds an extra layer of protection. Here’s a common workflow using PyTorch’s Opacus library.

Add noise to embedding vectors

Before passing synthetic data to a model, compute embeddings (e.g., using a fine-tuned BERT) and then apply differentially calculated noise.

import torch
import opacus

# Suppose embeddings is a tensor of shape (n_samples, embedding_dim)
embeddings = torch.randn(1000, 768)

# Configure privacy accountant
privacy_engine = opacus.PrivacyEngine()

# Add noise (sigma=1.0, batch_size=256)
private_embeddings = privacy_engine.make_private(
    module=torch.nn.Identity(),
    data_loader=[(embeddings, torch.zeros(embeddings.size(0)))],
    sigma=1.0,
    max_grad_norm=1.0
)
# private_embeddings now includes differentially calculated noise

Why noise is important

• Prevents inference of individual records even when synthetic data is combined with other sources.
• Maintains intact aggregate statistics, preserving dataset utility.
• Ensures compliance with regulatory guidelines: many jurisdictions accept differential privacy as a valid method for data sharing.

Best practices and ethical considerations

• Privacy by Design:Incorporate differential privacy from the start, not as an afterthought.
• Monitor privacy budget:Use privacy consumption calculation (epsilon) to track cumulative impact.
• Control bias:Verify that noise does not introduce systematic bias into model outcomes.
• Documentation:Record model parameters, prompts, and privacy calculations for reproducibility and compliance.

Practical examples

Use case 1: Healthcare simulation

Generate synthetic discharge notes while respecting patient privacy. Use an LLM with a prompt that includes dataset schema and privacy constraints. Add differentially calculated noise to clinical note embeddings before analysis.

Use case 2: Corporate data for marketing

Create customer interaction datasets to test new recommendation algorithms. Synthesize using an LLM that knows the CRM structure, then apply differential privacy to customer feature embeddings.

Final summary

Concrete actions to take

• Try an LLM with a structured prompt to generate your first synthetic dataset.
• Integrate Opacus or a similar differential privacy framework to add differentially calculated noise.
• Calculate privacy consumption (epsilon) after each iteration and keep documentation.
• Perform bias checks to ensure the synthetic dataset does not introduce systematic biases.
• Consider using open-source models (e.g., LLaMA 2) to maintain full control over the code.