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cs.LG2026
Advancing the State-of-the-Art in Empirical Privacy Auditing
Nicole Mitchell, Galen Andrew, Arun Ganesh +2
Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization of individual training examples. Empirical privacy auditing (EPA) quantifies th…
cs.LG2024
Fine-Tuning Large Language Models with User-Level Differential Privacy
Zachary Charles, Arun Ganesh, Ryan McKenna +4
We investigate practical and scalable algorithms for training large language models (LLMs) with user-level differential privacy (DP) in order to provably safeguard all the examples…