Generating synthetic data with differentially private LLM inference (Google Research, 2025)

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Learn how to generate synthetic data with differentially private LLM inference, a technique that balances data privacy and utility in AI applications

advanced Published 27 Jun 2026

Action Steps

Apply differential privacy (DP) to LLM inference using DP-SGD or other methods to protect sensitive data
Use private fine-tuning to generate high-quality synthetic data, but be aware of the high computational cost and data requirements
Implement private prediction methods to apply DP noise to LLM outputs without modifying the model, suitable for few-shot in-context learning and limited downstream applications
Evaluate the trade-off between privacy budget (ε) and the quality of generated synthetic data
Explore techniques to improve the scalability and efficiency of DP-based synthetic data generation for large-scale AI applications

Who Needs to Know This

Data scientists and AI engineers working on large language models (LLMs) and privacy-preserving machine learning can benefit from this technique to generate high-quality synthetic data while protecting sensitive information

Key Insight

💡 Differentially private LLM inference can generate high-quality synthetic data while protecting sensitive information, but requires careful consideration of privacy budget, computational cost, and scalability

Key Takeaways

Learn how to generate synthetic data with differentially private LLM inference, a technique that balances data privacy and utility in AI applications

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Title: Generating synthetic data with differentially private LLM inference (Google Research, 2025)

URL Source: https://medium.com/@martinyeunghk/generating-synthetic-data-with-differentially-private-llm-inference-google-research-2025-0056f5edeed8?source=rss------llm-5

Published Time: 2026-06-27T01:27:36Z

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# Generating synthetic data with differentially private LLM inference (Google Research, 2025)

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使用差分隱私 LLM 推論生成合成資料：深度分析與專案應用總結

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![Image 3](https://miro.medium.com/v2/resize:fit:700/1*cvnrtt3m78pnobiF5kw4YA.png)

### 核心問題與動機

在資料驅動的 AI 時代，隱私保護與資料可用性之間存在根本衝突。**差分隱私 (Differential Privacy, DP)** 提供數學嚴謹的保證，確保任何單一資料點的存在與否不會顯著影響輸出結果，從而保護個資。然而，將 DP 應用到大型語言模型 (LLM) 的情境特別具有挑戰性。

**主要問題：**

- **傳統私密微調 (Private Fine-Tuning)**：使用 DP-SGD 等方法在敏感資料上微調 LLM 參數，雖然能產生高品質合成資料，但計算成本極高（需處理數十億參數的模型）、資料需求門檻高，且不易擴展給多團隊使用。

- **先前私密預測 (Private Prediction)** 方法：僅對 LLM 輸出施加 DP 擾動（無需修改模型），但先前工作（如 Tang et al., 2024）只能在合理隱私預算（ε）下產生極少量合成樣本（<10 個），僅適用於 few-shot in-context learning，無法支援下游模型微調或大規模應用。

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Generating synthetic data with differentially private LLM inference (Google Research, 2025)

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