  Large language models (LLMs) are complex artificial intelligence systems
capable of understanding, generating and translating human language. They learn
language patterns by analyzing large amounts of text data, allowing them to
perform writing, conversation, summarizing and other language tasks. When LLMs
process and generate large amounts of data, there is a risk of leaking
sensitive information, which may threaten data privacy. This paper concentrates
on elucidating the data privacy concerns associated with LLMs to foster a
comprehensive understanding. Specifically, a thorough investigation is
undertaken to delineate the spectrum of data privacy threats, encompassing both
passive privacy leakage and active privacy attacks within LLMs. Subsequently,
we conduct an assessment of the privacy protection mechanisms employed by LLMs
at various stages, followed by a detailed examination of their efficacy and
constraints. Finally, the discourse extends to delineate the challenges
encountered and outline prospective directions for advancement in the realm of
LLM privacy protection.
