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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 9·5 min read
PAC-Private Autoregressive Generation: Calibrating Noise to Ensemble Disagreement
TL;DR
PAC-Private Autoregressive Generation introduces a method to enhance privacy in language models by calibrating noise based on output variability, achieving significant retention of fine-tuning gains while limiting privacy leakage.
✦ Why It Matters
Implement PAC-Private techniques to enhance privacy in your language model applications today.
Key Takeaways
- Explore PAC privacy to enhance model output security.
- Utilize overlapping worlds for better prediction stability.
- Limit privacy leakage while maintaining model performance.
- Consider the trade-offs between privacy and utility in model design.
How It Works
The method constructs 128 overlapping worlds from a private dataset, with each record appearing in half of these worlds. During generation, the public model proposes candidate tokens, and the worlds vote on these candidates.
The noise added to the predictions is calibrated based on the disagreement among the worlds, allowing for less noise when predictions are stable, thus enhancing privacy without sacrificing performance.