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cs.LG2025
FusionDP: Foundation Model-Assisted Differentially Private Learning for Partially Sensitive Features
Linghui Zeng, Ruixuan Liu, Atiquer Rahman Sarkar +3
Ensuring the privacy of sensitive training data is crucial in privacy-preserving machine learning. However, in practical scenarios, privacy protection may be required for only a su…
cs.LG2025
Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training
Toan Tran, Ruixuan Liu, Li Xiong
Large language models (LLMs) have become the backbone of modern natural language processing but pose privacy concerns about leaking sensitive training data. Membership inference at…