How to Generate Synthetic Data with Differential Privacy Using LLMs

Introduction: Why synthetic data and differential privacy matter today

Companies across every industry rely on high-quality data to train AI models, test systems, and inform decision-making. However, collecting real-world data poses compliance risks, such as GDPR violations, and can compromise user privacy. In this context,synthetic datadifferential privacy, it enables organizations to share valuable insights while safeguarding individuals’ personal information.

What role do new LLMs play in this workflow

In November 2026,Thinking Machines LablaunchedInkling 2 Small, a 594 billion-parameter MoE model with just 17 billion active parameters. Its efficiency makes it ideal for generating large volumes lisäksi of synthetic data without requiring extensive computing resources. Simultaneously,Together AIintroducedTogether Compiler, an agent that automates optimization of the generation process, making synthetic dataset creation faster and more adaptive. When a modern LLM like Inkling 2 Small is paired with differential privacy mechanisms, it can produce synthetic data that preserves underlying statistical distributions while adding mathematically guaranteed noise to protect each individual record.

Typical workflow: From real data to protected synthetic datasets

• Data curation:Gather a raw sample of real data (e.g., transaction records, customer profiles).
• Pre-processing:Tokenize or transform the data into a format suitable for the model.
• Private learning:Train a generator (such as Inkling 2 Small) using private gradient computation (DP-SGD) with an appropriate noise multiplier and maximum gradient norm.
• Generation:Use the trained model to create new records that reflect the statistics of the original dataset.
• Validation:Verify that the synthetic data meets the same patterns and constraints as the original data (e.g., using statistical parity, Kolmogorov-Smirnov test).

Practical implementation: A step-by-step example

Below is a Python code snippet demonstrating how to generate synthetic records using a modern LLM and the differential privacy frameworkOpacus. The example uses a text model; the same principle applies to multimodal LLMs like OpenAI’s GPT-Image-2.6 Flare when generating synthetic image data.

Generating synthetic image data with GPT-Image-2.6 Flare

When working with visual data, OpenAI’sGPT-Image-2.6 Flaremodel can generate synthetic images in just 6 seconds, supporting GIF and SVG exports. To preserve privacy, differential noise can be injected directly into the underlying diffusion model’s training process, ensuring that each generated image cannot be traced back to an individual.

• Build a synthetic image dataset using a base model like Inkling 2 Small for text descriptions, then pass the descriptions to Flare’s image generation model.
• Apply differential privacy during the diffusion model training phase (e.g., using Together AI’s DP framework with Together Compiler to accelerate the process).

Workflow example (pseudocode)

# Generate synthetic captions (privacy-protected)
captions = ["A cup of coffee on a table in a quiet café", "Red running shoes in a park", ...]

# Inject differential privacy noise during tokenization (simulation)
private_captions = add_differential_privacy_noise(captions, noise_multiplier=2.0)

# Use GPT-Image-2.6 Flare to generate images
for cap in private_captions:
    image_url = flare.generate_image(prompt=cap, duration=6)
    save_image(image_url, f"synthetic_img_{uuid4().hex}.png")

Why differential privacy ensures regulatory compliance

Differential privacy adds a mathematically rigorous statistical control that makes it impossible to definitively determine whether a single record is present in a dataset. This approach aligns with GDPR and CCPA guidelines, enabling organizations to share useful data while minimizing privacy risks.

Key takeaways

• Use modern, optimized LLMs (e.g., Inkling 2 Small) to reduce computing costs.
• Combine DP-SGD with frameworks like Opacus for quantifiable privacy guarantees.
• Leverage Together Compiler to accelerate DP training.
• For synthetic image data, use GPT-Image-2.6 Flare with a privacy-protected diffusion model.
• Always validate synthetic data with statistical tests before release.

Next steps: Integrating into your pipeline

1. Conduct a data auditto identify raw datasets that can be replaced with synthetic data.
2. Choose a suitable LLM(e.g., Inkling 2 Small) and a differential privacy framework (Opacus, TF-Privacy, or DP-Spark).
3. Implement a prototypeusing the code snippet above; start with a small sample to verify quality and privacy.
4. Monitor privacy metrics(epsilon budget) and data fidelity (perplexity, statistical similarity scores) as you scale up.
5. Document the processto demonstrate compliance with regulators.

Conclusion

Synthetic data and differential privacy together offer a powerful, future-ready solution to modern businesses’ data challenges. By leveraging cutting-edge LLMs like Inkling 2 Small and optimization tools like Together Compiler, organizations can generate realistic datasets that preserve underlying statistics while keeping individuals’ data safe. Integrating these techniques not only drives AI innovation but also demonstrates a tangible commitment to privacy, enabling secure and compliant growth in the era of generative AI. 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 (November 2026): Here are some recent trends and news to inspire you: - OpenAI Launches GPT-Image-2.6 Flare and Sunburst: Adds GIFs, SVG Export and 6-Second API Generation: OpenAI released Images 2.6 and the GPT-Image-2.6 Flare and Sunburst API models. Flare generates images in as little as 6 seconds, and both add GIF out... [2026-11-05] - Thinking Machines Releases Inkling 2 Small, a 594B MoE With 17B Active: Thinking Machines Lab released Inkling 2 Small, a 594B-parameter MoE with 17B active parameters. It scores 31 on the Artificial Analysis Intelligence ... [2026-11-06] - Baidu Releases ERNIE 5.5 Flash Base: A 280B MoE Base Model With 14B Active Parameters: Baidu has released ERNIE 5.5 Flash Base, a 280B sparse MoE base model with 14B active parameters, trained on 33.5T tokens and published before any mid... [2026-11-05] Use this current information as inspiration to create an original and relevant prompt for 2026.