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.
Position: Considerations for differentially private learning with large-scale public pretraining
3 Pith papers cite this work. Polarity classification is still indexing.
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Empirical benchmarks show distribution similarity between adaptation and pretraining data increases practical privacy leakage in DP-adapted LLMs at fixed theoretical guarantees, with LoRA providing strongest protection for OOD cases.
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.
citing papers explorer
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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.
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Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
Empirical benchmarks show distribution similarity between adaptation and pretraining data increases practical privacy leakage in DP-adapted LLMs at fixed theoretical guarantees, with LoRA providing strongest protection for OOD cases.
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From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.