Differential Privacy for LLM Training: Protecting Data at Scale

LLMs can memorize sensitive data if training pipelines are not controlled.

Differential privacy provides formal guarantees against memorization and leakage.

Privacy comes at a cost: adding noise can reduce accuracy.

The goal is to balance epsilon budgets with acceptable performance.

Use gradient clipping, noise injection, and privacy accounting.

Track privacy budgets across epochs to avoid silent overuse.

Test for memorization using canary strings and leakage probes.

Evaluate on real tasks to verify utility remains acceptable.

Document privacy settings and budgets for auditability.

Align privacy guarantees with compliance requirements and user expectations.

Separate DP training configs from non-DP configs to prevent accidental leakage.

Test privacy accounting on small runs before scaling to full training jobs.

Keep a clear mapping between epsilon budgets and product requirements so trade-offs are explicit.

Use automated checks to ensure privacy accounting resets between experiments.

Document privacy budget consumption per run so audits can verify compliance.

Keep a private training log of DP parameters to support internal audits and reviews.

Validate that DP settings are enforced in distributed training so no worker bypasses controls.

Maintain a checklist for DP training runs so steps are repeatable across teams.

Track privacy budget usage per dataset to prevent uneven exposure.

Require approval for any change to privacy accounting code paths.

Review DP training metrics after each run to confirm privacy targets were met.

Include DP tests in CI so configuration regressions are caught early.

Store DP configs in version control to make changes traceable.

Minimize data retention windows so sensitive samples are not stored longer than needed.

Keep dataset access logs so privacy reviews can trace who touched sensitive data.

Does DP always reduce quality? It can, but careful tuning keeps losses manageable.

Is DP required for all models? Not always, but it is essential for sensitive domains.

What is a safe starting point? Start with conservative budgets and measure leakage.