Threats, attacks, and defenses in machine unlearning: A survey
Machine Unlearning (MU) has recently gained considerable attention due to its potential to
achieve Safe AI by removing the influence of specific data from trained Machine Learning …
achieve Safe AI by removing the influence of specific data from trained Machine Learning …
The emerged security and privacy of llm agent: A survey with case studies
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 …
evolved to perform complex tasks. LLM agents are now extensively applied across various …
When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
The deployment of large language models (LLMs) like ChatGPT and Gemini has shown
their powerful natural language generation capabilities. However, these models can …
their powerful natural language generation capabilities. However, these models can …
TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents
Reinforcement learning (RL) trains an agent from experiences interacting with the
environment. In scenarios where online interactions are impractical, offline RL, which trains …
environment. In scenarios where online interactions are impractical, offline RL, which trains …
Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning
Duplication is a prevalent issue within datasets. Existing research has demonstrated that the
presence of duplicated data in training datasets can significantly influence both model …
presence of duplicated data in training datasets can significantly influence both model …
Evaluating of Machine Unlearning: Robustness Verification Without Prior Modifications
Machine unlearning, a process enabling pre-trained models to remove the influence of
specific training samples, has attracted significant attention in recent years. While extensive …
specific training samples, has attracted significant attention in recent years. While extensive …