摘要
Abstract
The rapid development of large language models(LLM)has been greatly facilitated by the iterative up-grading of artificial intelligence technologies,and they have been widely applied across various industries.However,due to their massive parameter scales and data training approaches based on vast amounts of Web corpora,numerous privacy leakage risks have been faced by LLM throughout their entire lifecycle.To address this issue,a systematic analysis was conducted along the main line of"privacy risk analysis-privacy risk measurement-privacy protection technology"throughout the LLM lifecycle.Firstly,Six types of privacy threats,including data extraction,member-ship inference,content injection,attribute inference,prompt stealing,and jailbreak attacks were systematically ana-lyzed during the stages of data preparation,model training,and inference interaction.Secondly,The quantitative evaluation methods for these six types of privacy threats,namely privacy measurement techniques,were focused on,so as to scientifically assess the privacy threats faced by LLM.Finally,A detailed introduction was also given to the privacy protection technology system throughout the entire LLM lifecycle,covering key technologies such as differ-ential privacy,federated learning,and secure multi-party computation.The research findings presented in this paper can be provided as comprehensive theoretical and practical references for privacy and security in the design,training,and application of LLM,and an effective balance can be facilitated between the powerful capabilities of LLM and user privacy protection.关键词
大语言模型/隐私风险/隐私度量/隐私保护技术Key words
large language model/privacy risk/privacy measurement/privacy protection technology分类
信息技术与安全科学
