Privacy dilemmas and opportunities in large language models: A brief review

Hongyi Li, Jiawei Ye, Wu Jie

Abstract: The growing number of cases indicates that large language model (LLM) brings transformative advancements while raising privacy concerns. Despite promising recent surveys proposed in the literature, there is still a lack of comprehensive analysis dedicated to text privacy specifically for LLM. By comprehensively collecting LLM privacy research, we summarize five privacy issues and their corresponding solutions during both model training and invocation and extend our analysis to three research focuses in LLM application. Moreover, we propose five further research directions and provide prospects for LLM native security mechanisms. Notably, we find that most LLM privacy research is still in the technical exploration phase, with the hope that this work can assist in LLM privacy development.