How to Generate Synthetic Data with Differential Privacy Using Modern LLMs

How to Generate Synthetic Data While Respecting Differential Privacy with Modern LLMs

Over the past few months, the generative AI landscape has been enriched with cutting-edge models such as DeepMind’s Gemini 4 Carbon, NVIDIA’s Nemotron 3.5 Transcribe, and Block’s new Dessa speech model. These tools offer unprecedented opportunities to create synthetic data, but the challenge remains: how can we ensure that the generated data does not reveal personal information? The answer lies in differential privacy, a mathematical method that adds measurable noise to dataset statistics.

Why Differential Privacy Is Essential Today

When you combine powerful LLMs with differential privacy, you can:

• Create realistic synthetic records for testing, development, or sharing with third parties.
• Maintain the statistical utility of the dataset thanks to calibrated privacy techniques.
• Comply with regulatory requirements without resorting to drastic identifier removal methods.

Below is a complete, production-ready example that demonstrates how to use a modern LLM (e.g., Gemini 4 Carbon) together with a differential privacy library such as IBM’s diffprivlib.

A well-designed prompt is the first step. To generate a synthetic customer dataset, you can ask the model to produce structured records:

"""
Generate a synthetic customer record with the following attributes:
- age (integer between 18 and 70)
- annual income (integer between 30k and 150k USD)
- postal code (5 digits)
- gender (male, female, other)
- purchase history (list of 3-5 products)
Return everything as JSON.
"""

This prompt is specific, safe, and leverages the model’s ability to understand the desired structure.

Run the prompt multiple times (e.g., 1000 times) to create a raw dataset. Here is a Python snippet that uses Hugging Face’s chat pipeline with a Gemini-compatible model:

import requests
import json
import pandas as pd

# Replace with your actual model endpoint
API_URL = "https://api.gemini.ai/v1/chat"
HEADERS = {"Authorization": "Bearer YOUR_API_KEY", "Content-Type": "application/json"}

prompt = """
Generate a synthetic customer record with the following attributes:
- age (integer between 18 and 70)
- annual income (integer between 30k and 150k USD)
- postal code (5 digits)
- gender (male, female, other)
- purchase history (list of 3-5 products)
Return everything as JSON.
"""

records = []
for i in range(1000):
    payload = {"model": "gemini-4-carbon", "messages": [{"role": "user", "content": prompt}]}
    resp = requests.post(API_URL, headers=HEADERS, json=payload)
    data = resp.json()
    # Extract JSON from model response
    json_str = data["choices"][0]["message"]["content"]
    records.append(json.loads(json_str))

# Save raw dataset
synth_df = pd.DataFrame(records)
synth_df.to_csv("synthetic_customers_raw.csv", index=False)
print(f"Generated {len(synth_df)} raw records.")

Now you need to add a statistical control. Using diffprivlib, you can calculate protected statistics such as the mean income or postal code distribution:

from diffprivlib.mechanisms import GaussianMechanism
from diffprivlib.utils import PrivacyReport

# Privacy parameters (epsilon, delta)
epsilon = 1.0
delta = 1e-5

# Example: add noise to mean income
mean_income = synth_df["annual_income"].mean()
mechanism = GaussianMechanism(epsilon=epsilon, delta=delta, sensitivity=1000)
protected_mean = mechanism.transform(mean_income)

print(f"Raw mean income: {mean_income}")
print(f"Protected mean income: {protected_mean}")

# Generate a privacy report
report = PrivacyReport()
report.add_mechanism(mechanism)
print(report.summarize())

You can also protect the entire dataset by adding noise to each column using a mechanism based on RandomizedResponse or a more sophisticated sensitivity adjustment. The goal is to maintain statistical utility while respecting a predetermined epsilon level.

Practical Tips for Maintaining Utility

• Choose the right epsilon: Lower epsilon values (e.g.,
• Use privacy-optimized models: Models like Nemotron 3.5 Transcribe are open weight and have been trained with built-in differential privacy techniques, making them ideal for synthetic data generation.
• Leverage speech-driven synthesis: The Dessa model can generate realistic synthetic narratives that can be transcribed into structured text, providing an additional way to enrich datasets.
• Validate: Always compare protected distributions with raw ones using overlay plots or statistical tests (e.g., KS test) to ensure the models are not overly distorted.

Real-World Use Cases in 2026

1. Healthcare Research

Healthcare companies need realistic patient data for training predictive models, but must comply with strict privacy regulations. By generating synthetic medical records with Gemini 4 Carbon and adding differential control, hospitals can share useful datasets without exposing personal information.

2. eCommerce Market Analysis

Retailers create synthetic datasets of customer purchase behavior to test new recommendation algorithms. Using Nemotron 3.5 Transcribe to transcribe synthetic customer-agent dialogues and applying differential privacy to aggregated metrics, they maintain customer trust and comply with regulations.

3. Security Simulation

Incident response teams need synthetically realistic network traffic to test defense systems. The Gemini 4 Carbon model can generate detailed packet descriptions and Dessa can synthesize conversations between attackers and defenders, all protected with differential privacy mechanisms.

Concrete Actions to Take

1. Design a prompt that produces structured JSONwith the required columns.
2. Run generation in batch
3. Save the raw datasetfor reference, then apply differential privacy control to the statistics you intend to share.
4. Document privacy parameters(epsilon, delta) in a README file to ensure reproducibility.
5. Test utilityby comparing raw and protected distributions; adjust epsilon if utility loss is unacceptable.

Conclusion