8 citations · 21 across the 17 of their papers we have counts for
8 papers · 1 filter
Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI
Dayong Ye, Tianqing Zhu, Shang Wang +4
Generative AI technology has become increasingly integrated into our daily lives, offering powerful capabilities to enhance productivity. However, these same capabilities can be ex…
Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning
Dayong Ye, Tianqing Zhu, Jiayang Li +5
Duplication is a prevalent issue within datasets. Existing research has demonstrated that the presence of duplicated data in training datasets can significantly influence both mode…
Defending Against Neural Network Model Inversion Attacks via Data Poisoning
Shuai Zhou, Dayong Ye, Tianqing Zhu +1
Model inversion attacks pose a significant privacy threat to machine learning models by reconstructing sensitive data from their outputs. While various defenses have been proposed…
When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
Shang Wang, Tianqing Zhu, Dayong Ye +1
The deployment of large language models (LLMs) like ChatGPT and Gemini has shown their powerful natural language generation capabilities. However, these models can inadvertently le…
The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies
Feng He, Tianqing Zhu, Dayong Ye +3
Inspired by the rapid development of Large Language Models (LLMs), LLM agents have evolved to perform complex tasks. LLM agents are now extensively applied across various domains,…
Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning
Xuhan Zuo, Minghao Wang, Tianqing Zhu +4
The development of Large Language Models (LLMs) faces a significant challenge: the exhausting of publicly available fresh data. This is because training a LLM needs a large demandi…
Unique Security and Privacy Threats of Large Language Models: A Comprehensive Survey
Shang Wang, Tianqing Zhu, Bo Liu +4
With the rapid development of artificial intelligence, large language models (LLMs) have made remarkable advancements in natural language processing. These models are trained on va…
Reinforcement Unlearning
Dayong Ye, Tianqing Zhu, Congcong Zhu +6
Machine unlearning refers to the process of mitigating the influence of specific training data on machine learning models based on removal requests from data owners. However, one i…