How to Generate Synthetic Data with Differential Privacy Using LLMs in 2026

Introduction: Why synthetic data and differential privacy are essential today

Companies across every industry rely on high-quality datasets to train AI models. However, sharing real data exposes them to privacy risks and regulatory challenges. In 2026, the combination of synthetic data and differential privacy offers a practical solution: creating realistic data that protects every individual.

This article explainshowto use modern LLMs (such as GLM-5.3-Flash and Qwen3.8-Flash-Next) alongside differential privacy libraries to generate secure synthetic data. It includes practical examples and ready-to-use code.

What are synthetic data and differential privacy?

Synthetic data: a quick overview

• Artificially created datasets that mimic the structure and distributions of real data.
• Ideal for testing, model development, and sharing data without exposing personal information.

Differential privacy: the hidden protector

• Adds statistical noise to queries or training processes, making it impossible to identify any single record.
• Provides quantifiable protection, essential for compliance with GDPR, CCPA, and future regulations.

Combining these two techniques creates a virtuous cycle: synthetic data maintains model utility, while differential privacy adds an extra layer of security.

Current LLM tools and models (2026)

The latest LLMs offer multimodal capabilities and code generation that simplify the creation of synthetic data pipelines.

• GLM-5.3-Flash
• Qwen3.8-Flash-Next
• Gemini Omni 1.1 Flash

These models can be paired with differential privacy frameworks likeOpacus(PyTorch) orTensorFlow Privacyto add the necessary protection layer.

Step-by-step workflow: Generate synthetic data with differential privacy

1. Prepare your real data (the starting point)

Upload your existing dataset, ensuring it is clean and of appropriate size for training.

import pandas as pd

data = pd.read_csv('customer_data.csv')
print(data.head())
# Output: DataFrame with columns: id, age, income, purchase_history

2. Generate synthetic data with an LLM

Use an LLM like GLM-5.3-Flash via a simple wrapper. The following example uses a generic API call; adapt the endpoint based on your provider.

import requests
import json

prompt = """
Generate 1000 rows of synthetic data that reflect the distribution of:
- id (unique integers)
- age (normal, mean 45, std 12)
- income (log-normal, mean 50000)
- purchase_history (numeric, 0-10)
Return only CSV.
"""

payload = {
    "model": "GLM-5.3-Flash",
    "messages": [{"role": "user", "content": prompt}],
    "temperature": 0.2,
    "max_tokens": 2000
}

response = requests.post('https://api.glm.ai/v1/chat/completions',
                         headers={'Authorization': 'Bearer YOUR_KEY'},
                         json=payload)
synthetic_csv = response.json()['choices'][0]['message']['content']
synthetic_df = pd.read_csv(pd.io.common.StringIO(synthetic_csv))
print(synthetic_df.head())

3. Apply differential privacy

Even synthetic data benefits from differential privacy, which adds an extra layer of security. Opacus is a popular library for PyTorch.

from opacus import PrivacyEngine

# Assume we want to train a simple linear regression model
model = torch.nn.Linear(data.shape[1], 1)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

privacy_engine = PrivacyEngine()
model, optimizer, data_loader = privacy_engine.make_private(
    module=model,
    optimizer=optimizer,
    data_loader=data,
    noise_multiplier=1.0,      # amount of Gaussian noise added
    max_grad_norm=1.0,          # limit for gradient clipping
)

# Train the model on synthetic (or real) data
for epoch in range(5):
    for batch in data_loader:
        optimizer.zero_grad()
        output = model(batch)
        loss = ((output - batch) ** 2).sum()
        loss.backward()
        optimizer.step()

# After training, print the privacy accounts
print(privacy_engine.get_epsilon(delta=1e-5))

The `get_epsilon` function returns the ε (epsilon) parameter, which quantifies privacy level. Lower values indicate stronger privacy.

4. Validate utility and privacy

• Use statistical metrics (KS test, correlation) to verify that synthetic data maintains the original distribution.
• Perform privacy checks such as sensitivity testing or differential privacy verification using Opacus metrics.

from scipy.stats import ks_2samp

# Distribution of income column in original vs synthetic data
ks_stat, p_value = ks_2samp(data['income'], synthetic_df['income'])
print(f"KS stat: {ks_stat:.4f}, p-value: {p_value:.4f}")
# A high p-value (>0.05) suggests the two distributions are similar.

Practical exercises and tips

• Use multimodal models: Gemini Omni 1.1 Flash can generate privacy-compliant image captions or synthetic videos. Convert the output into structured text for further synthesis.
• Control noise: Start with a low noise_multiplier (e.g., 0.5) and gradually increase it to find the right balance between utility and privacy.
• Document ε: Keep the ε value returned by Opacus for each run; it is essential for regulatory compliance.
• Use synthetic data vaults: Platforms like DataCoup or Google Data Commons offer ready-to-use environments that combine LLM generation with built-in differential privacy.

Final takeaway

• Generating synthetic data with modern LLMs (GLM-5.3-Flash, Qwen3.8-Flash-Next, Gemini Omni 1.1 Flash) allows you to maintain model utility without exposing real data.
• Adding differential privacy through tools like Opacus ensures quantifiable protection, maintaining trust and regulatory compliance.

Useful resources and next steps

• Documentation forOpacusandTensorFlow Privacy.
• Paper on"Differential Privacy for Synthetic Data Generation"
• Open-source repository:github.com/yourorg/synth-priv-fair

Start today with a small dataset, add a layer of differential privacy, and observe how synthetic data maintains model utility while protecting personal information.

With this workflow, you’ll be equipped to leverage the best of LLMs in 2026 while meeting the most stringent privacy expectations.

IMPORTANT: We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present. CURRENT CONTEXT (August 2026): Here are some recent trends and news to inspire you: - VentureBeat names Rob Strechay as its first Lead Analyst, expanding its enterprise AI research push: Rob Strechay, until recently managing director and principal analyst at theCUBE Research, has joined VentureBeat as our first Lead Analyst and a found... [2026-08-19] - A quarter of Nvidia's business next year comes from labs it is financing: Nvidia has put nearly US$50 billion into the AI labs that buy its chips, and has lined up commitments for more than $500 billion Colette Kress, the co... [2026-08-27] - Gatik raises $200M to scale AI-powered autonomous freight: Autonomous trucking company Gatik has raised $200 million in Series D funding to expand its driverless freight operations across North America. The ro... [2026-08-26] Use this current information as inspiration to create an original and relevant prompt for 2026.