How to Generate Synthetic Data with LLMs While Respecting Differential Privacy

Introduction: why synthetic data generation is crucial today

Companies across all industries need highâ€'quality data to train increasingly performant AI models. However, collecting real data exposes them to compliance and privacy risks. The most promising solution is synthetic data, i.e. artificially generated information that retains the useful statistics of the original set without exposing personal information. In 2026, the adoption of this technology is growing rapidly, thanks to more advanced LLMs and mature differential privacy techniques.

Why differential privacy is the right choice for synthetic data

Differential privacy adds mathematically guaranteed noise to analytical results, making it impossible to identify individual people. When generating synthetic data with language models, applying this guarantee means:

• Protecting sensitive information while keeping underlying statistical patterns intact.
• Meeting strict regulations such as GDPR and CCPA without resorting to complex anonymization procedures.
• Building trust with customers and partners thanks to quantifiable protection.

Below we explore how to combine LLMs and differential privacy in a practical way.

How to generate synthetic data with LLMs while preserving differential privacy

The typical workflow is divided into four steps:

1. Raw data preparation: collect a representative sample of real data (e.g. transactions, interaction logs).
2. Model learning with DPâ€'SGD: train a generative model using differentially private stochastic gradient descent (DPâ€'SGD) to add noise during optimization.
3. Synthetic generation: use the trained model (often a fineâ€'tuned LLM) to create new examples.
4. Validation: verify that the synthetic data retains key distributions and does not leak personal information.

Each of these steps can be implemented with the most common openâ€'source libraries.

Practical example: generating synthetic data with PyTorch, Opacus and an LLM

Below is a snippet showing how to combine PyTorch, the Opacus DPâ€'SGD library and a small fineâ€'tuned GPTâ€'2 model to produce synthetic records.

# Install dependencies (run once)
!pip install torch torchvision opacus transformers datasets

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from opacus import PrivacyEngine
from opacus.utils import module_modification

# 1️âÆ'£ Load a pretrained tokenizer and model
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# 2️âÆ'£ Prepare a small sample dataset (replace with your own data)
raw_texts = [
    "Customer purchased 3 products for a total of €45.99",
    "12â€'day payment delay on invoice 2023",
    "Request for size M exchange on order #4582"
]
encodings = tokenizer(raw_texts, truncation=True, padding=True, max_length=128, return_tensors="pt")

# 3️âÆ'£ Configure DPâ€'SGD
batch_size = 8
lr = 5e-5
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
privacy_engine = PrivacyEngine()
model, optimizer, _ = privacy_engine.attach(
    module=model,
    batch_size=batch_size,
    sample_size=len(raw_texts),
    epochs=3,
    epsilon=1.0,  # privacy budget
    max_grad_norm=1.0,
)

# 4️âÆ'£ Train the (generative) model â€" simplified here
model.train()
for epoch in range(3):
    for i in range(0, len(raw_texts), batch_size):
        batch = {k: v[i:i+batch_size] for k, v in encodings.items()}
        outputs = model(**batch, labels=batch['input_ids'])
        loss = outputs.loss
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
    print(f"Epoch {epoch+1} completed")

# 5️âÆ'£ Generate synthetic data
example_input = tokenizer.encode("Customer:", return_tensors="pt")
with torch.no_grad():
    synthetic_ids = model.generate(
        example_input,
        max_length=50,
        num_return_sequences=5,
        temperature=0.9,
        do_sample=True,
        top_p=0.9
    )

synthetic_texts = tokenizer.batch_decode(synthetic_ids, skip_special_tokens=True)
print("Generated synthetic data:")
for txt in synthetic_texts:
    print(" -", txt)

This script illustrates an endâ€'toâ€'end workflow that adds noise during training (thanks to Opacus) and produces realistic yet synthetic descriptions. Adjusting `epsilon` allows you to balance utility and privacy.

Best practices and checklist for secure synthetic data

• Define a low epsilon budget (e.g. ε ≤ 2) to meet regulatory standards.
• Use version control on raw and synthetic datasets for auditability.
• Apply postâ€'processing (e.g. data integrity checks) before releasing synthetic data.
• Benchmark key statistics (mean, variance, distributions) between real and synthetic data.
• Document the training process, including hyperâ€'parameters, for reproducibility.

Current trends in 2026 that favor this combination

The market is seeing three related developments:

• Privacyâ€'optimized foundation models â€" many distributed LLMs now include builtâ€'in differential privacy training modes, reducing the engineering overhead for practitioners.
• Cloudâ€'scale synthetic data platforms â€" services like AWS Synthetic Data Generator and Azure Differential Privacy Service simplify the creation of trainingâ€'ready datasets.
• Realâ€'world applications â€" companies such as CloudNC, Palantir Foundry and JD.com are using synthetic data to optimize supply chains, logistics and robotics. JD.com, for instance, has launched a Physical AI Acceleration Plan that now deploys over 3 million robots across its logistics network, showcasing the scalability of the approach.
• AIâ€'driven evaluation and resilience â€" Anthropic’s new plugin evals for Claude Code provide robust testing frameworks for LLMâ€'based synthetic data generation, while recent AI agents can detect and remediate supplyâ€'chain disruptions in real time, as highlighted by the J.S. Held Global Risk Report (2025 impact: $184 B).

These trends make privacyâ€'preserving synthetic data generation not only an ethical choice but also a competitive strategy.

Conclusion: transform raw data into a secure asset

The combination of advanced LLMs and differential privacy enables organizations to unlock the value of data without compromising individuals’ privacy. By following the practical workflow outlined above and adopting the best practices, teams can produce synthetic data that retains statistical usefulness, complies with regulations and increases stakeholder trust.

Concrete actions to take today

• Assess your current epsilon budget against GDPR/CCPA requirements.
• Design a DPâ€'SGD prototype using PyTorch + Opacus on a small dataset.
• Fineâ€'tune an LLM with builtâ€'in differential privacy mode (if available).
• Implement a data integrity check to verify that synthetic data retains key statistics.
• Document the entire process for future audits and compliance.