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cs.LG2026
Provably Protecting Fine-Tuned LLMs from Training Data Extraction while Preserving Utility
Tom Segal, Asaf Shabtai, Yuval Elovici
Fine-tuning large language models (LLMs) on sensitive datasets raises privacy concerns, as training data extraction (TDE) attacks can expose highly confidential information. Existi…
cs.LG2024
DOMBA: Double Model Balancing for Access-Controlled Language Models via Minimum-Bounded Aggregation
Tom Segal, Asaf Shabtai, Yuval Elovici
The utility of large language models (LLMs) depends heavily on the quality and quantity of their training data. Many organizations possess large data corpora that could be leverage…