9 papers
Beyond Indistinguishability: Measuring Extraction Risk in LLM APIs
Ruixuan Liu, David Evans, Li Xiong
Indistinguishability properties such as differential privacy bounds or low empirically measured membership inference are widely treated as proxies to show a model is sufficiently p…
LLM-Driven Online Aggregation for Unstructured Text Analytics
Chao Hui, Weizheng Lu, Yanjie Gao +3
Large Language Models (LLMs) exhibit strong capabilities in text processing, and recent research has augmented SQL and DataFrame with LLM-powered semantic operators for data analys…
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…
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…