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Differential Privacy in LLMs

DP adds calibrated noise to training, preventing memorization of individual data points.

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Implementation Steps

  1. Setup Opacus (PyTorch DP lib):
    from opacus import PrivacyEngine
privacy_engine = PrivacyEngine()
model, optimizer, dataloader = privacy_engine.make_private(
    module=model, optimizer=optimizer, data_loader=dataloader,
    noise_multiplier=1.1, max_grad_norm=1.0
)
  2. Train with DP:
    • Standard loop with privacy engine.
  3. Tune Hyperparams:
    • Balance epsilon (e.g., 1-10) with accuracy.
  4. Inference: No changes; privacy in training.
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