6 papers · 1 filter
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…
FedSGT: Exact Federated Unlearning via Sequential Group-based Training
Bokang Zhang, Hong Guan, Hong kyu Lee +3
Federated Learning (FL) enables collaborative, privacy-preserving model training, but supporting the "Right to be Forgotten" is especially challenging because data influences the m…
SnapAudit: Active Auditing of Differentially Private In-Context Learning via Snapshot-Based Simulation
Yuyang Xia, Ruixuan Liu, Li Xiong
In-context learning (ICL) allows LLMs to adapt to new tasks via a few demonstrations, but those demonstrations may contain sensitive data. Differentially private (DP) ICL mechanism…
Direct Token Optimization: A Self-contained Approach to Large Language Model Unlearning
Hong kyu Lee, Ruixuan Liu, Li Xiong
Machine unlearning is an emerging technique that removes the influence of a subset of training data (forget set) from a model without full retraining, with applications including p…
UniAud: A Unified Auditing Framework for High Auditing Power and Utility with One Training Run
Ruixuan Liu, Li Xiong
Differentially private (DP) optimization has been widely adopted as a standard approach to provide rigorous privacy guarantees for training datasets. DP auditing verifies whether a…
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…