1 citations · 1 across the 2 of their papers we have counts for
4 papers
Membership Inference via Pairwise Likelihood Ratios
Shengjie Niu, Zebin Yun, Yeheng Ge +1
Membership inference attacks (MIAs) are the standard tool for auditing the privacy risks of machine learning models. Given a query point, an MIA aims to determine whether that poin…
Exploring Membership Inference Vulnerabilities in Clinical Large Language Models
Alexander Nemecek, Zebin Yun, Zahra Rahmani +4
As large language models (LLMs) become progressively more embedded in clinical decision-support, documentation, and patient-information systems, ensuring their privacy and trustwor…
Privacy-Preserving Collaborative Genomic Research: A Real-Life Deployment and Vision
Zahra Rahmani, Nahal Shahini, Nadav Gat +7
The data revolution holds significant promise for the health sector. Vast amounts of data collected from individuals will be transformed into knowledge, AI models, predictive syste…
The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data Augmentations
Zebin Yun, Achi-Or Weingarten, Eyal Ronen +1
To help adversarial examples generalize from surrogate machine-learning (ML) models to targets, certain transferability-based black-box evasion attacks incorporate data augmentatio…