Controlled experiments across 96 LoRA adapters show that reduced optimizer updates explain nearly all observed memorization drops in DP-SGD fine-tuning, HMAC pseudonymization cuts exposure 40-61% without creating new targets, and 1-3B models achieve only 0.19-0.28 F1 under the tested budget.
Leaner Training, Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA
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Decomposing Memorization Reduction in Privacy-Preserving Fine-Tuning of SLMs for CSIRTs
Controlled experiments across 96 LoRA adapters show that reduced optimizer updates explain nearly all observed memorization drops in DP-SGD fine-tuning, HMAC pseudonymization cuts exposure 40-61% without creating new targets, and 1-3B models achieve only 0.19-0.28 F1 under the tested budget.
- LLM Harms: A Taxonomy and Discussion