4 papers · 1 filter
COMPASS: Membership Inference Attack Against Vision Large Language Models via Adaptive Noise Calibration
Damien Lo, Hong Kyu Lee, Ruixuan Liu +1
Vision large language models (VLLMs) are deployed in privacy-sensitive domains, raising training-data exposure concerns. Existing gray-box membership inference attacks (MIAs) commo…
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…
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…
Differentially Private Tabular Data Synthesis using Large Language Models
Toan V. Tran, Li Xiong
Synthetic tabular data generation with differential privacy is a crucial problem to enable data sharing with formal privacy. Despite a rich history of methodological research and d…