3 papers
cs.CR2026
Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
Jie Fu, Nima Naderloui, Da Zhong +2
Machine unlearning (MU) has emerged as a key mechanism for ensuring data privacy and regulatory compliance by enabling models to forget specific training samples. However, recent s…
cs.CR2025
SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses
Hanbin Hong, Shuang Wu, Shuya Feng +6
Prompt-security claims for large language models (LLMs) are often compared as properties of individual attacks or defenses, although measured harmfulness depends on the target mode…
cs.CR2025
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Nima Naderloui, Shenao Yan, Binghui Wang +4
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning en…