8 papers · 1 filter
Auditing the Privacy of Synthetic Gene Expression Data: A Unified Weighted-Distance Framework for No-Box Membership Inference
Owen Tucker, Lily Wang, Harutoshi Okumura +2
Synthetic gene expression data is increasingly proposed as a privacy-preserving substitute for controlled-access genomic repositories, but its safety depends on empirical auditing.…
PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaboration
Yuyang Xia, Ruixuan Liu, Li Xiong
Graph inference over relational data can expose sensitive edge information, and this risk becomes more severe in dynamic graphs, where repeated model updates cause privacy loss to…
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…
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…
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…
ExpShield: Safeguarding Web Text from Unauthorized Crawling and LLM Exploitation
Ruixuan Liu, Toan Tran, Tianhao Wang +3
As large language models increasingly memorize web-scraped training content, they risk exposing copyrighted or private information. Existing protections require compliance from cra…
PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps
Ruixuan Liu, Tianhao Wang, Yang Cao +1
The pre-training and fine-tuning paradigm has demonstrated its effectiveness and has become the standard approach for tailoring language models to various tasks. Currently, communi…