2 citations · 5 across the 5 of their papers we have counts for
5 papers
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…
AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems
Zhiyu Zhu, Zhibo Jin, Hongsheng Hu +5
AI systems, in particular with deep learning techniques, have demonstrated superior performance for various real-world applications. Given the need for tailored optimization in spe…
Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning
Binhao Ma, Tianhang Zheng, Hongsheng Hu +5
Machine learning models trained on vast amounts of real or synthetic data often achieve outstanding predictive performance across various domains. However, this utility comes with…
Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning
Hongsheng Hu, Shuo Wang, Tian Dong +1
Machine unlearning has become a promising solution for fulfilling the "right to be forgotten", under which individuals can request the deletion of their data from machine learning…
A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services
Hongsheng Hu, Shuo Wang, Jiamin Chang +5
The right to be forgotten requires the removal or "unlearning" of a user's data from machine learning models. However, in the context of Machine Learning as a Service (MLaaS), retr…