Large language models privacy and security

Abstract

The advancement of large language models (LLMs) has yielded significant advancements across various domains. Nevertheless, this progress has also raised crucial concerns regarding privacy and security. The paper does a comprehensive literature study to thoroughly examine the fundamental principles of LLM. It also provides a detailed examination of the characteristics and application fields of various LLMs, with a particular focus on Transformer. Furthermore, this study places emphasis on the examination of privacy concerns that may emerge in the context of LLM's handling of personal and sensitive data. It also explores the potential hazards associated with information leakage and misuse, as well as the existing privacy safeguards and the obstacles encountered in their implementation. Overall, LLM has made significant advancements in technology. However, it is imperative to acknowledge the importance of doing research on safeguarding privacy and enhancing security. These aspects are vital for guaranteeing the sustained development and public confidence in LLM technology.