On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review
Large Language Models (LLMs) are complex artificial intelligence systems, which can understand, generate, and translate human languages. By analyzing large amounts of textual data, these models learn language patterns to perform tasks such as writing, conversation, and summarization. Agents built on...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | High-Confidence Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667295225000042 |
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| _version_ | 1849433501541072896 |
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| author | Biwei Yan Kun Li Minghui Xu Yueyan Dong Yue Zhang Zhaochun Ren Xiuzhen Cheng |
| author_facet | Biwei Yan Kun Li Minghui Xu Yueyan Dong Yue Zhang Zhaochun Ren Xiuzhen Cheng |
| author_sort | Biwei Yan |
| collection | DOAJ |
| description | Large Language Models (LLMs) are complex artificial intelligence systems, which can understand, generate, and translate human languages. By analyzing large amounts of textual data, these models learn language patterns to perform tasks such as writing, conversation, and summarization. Agents built on LLMs (LLM agents) further extend these capabilities, allowing them to process user interactions and perform complex operations in diverse task environments. However, during the processing and generation of massive data, LLMs and LLM agents pose a risk of sensitive information leakage, potentially threatening data privacy. This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding. Specifically, we conduct an in-depth survey about privacy threats, encompassing passive privacy leakage and active privacy attacks. Subsequently, we introduce the privacy protection mechanisms employed by LLMs and LLM agents and provide a detailed analysis of their effectiveness. Finally, we explore the privacy protection challenges for LLMs and LLM agents as well as outline potential directions for future developments in this domain. |
| format | Article |
| id | doaj-art-66845a89f5734c8c8ae8912e0e3655a9 |
| institution | Kabale University |
| issn | 2667-2952 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | High-Confidence Computing |
| spelling | doaj-art-66845a89f5734c8c8ae8912e0e3655a92025-08-20T03:27:01ZengElsevierHigh-Confidence Computing2667-29522025-06-015210030010.1016/j.hcc.2025.100300On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature reviewBiwei Yan0Kun Li1Minghui Xu2Yueyan Dong3Yue Zhang4Zhaochun Ren5Xiuzhen Cheng6School Computer Science and Technology, Shandong University, Qingdao 266237, ChinaSchool Computer Science and Technology, Shandong University, Qingdao 266237, ChinaSchool Computer Science and Technology, Shandong University, Qingdao 266237, China; Corresponding authors.School Computer Science and Technology, Shandong University, Qingdao 266237, ChinaDepartment of Computer Science, Drexel University, Philadelphia 19104, USA; Corresponding authors.Leiden Inst of Advanced Computer Science, Leiden University, Leiden 2333 CC, NetherlandsSchool Computer Science and Technology, Shandong University, Qingdao 266237, ChinaLarge Language Models (LLMs) are complex artificial intelligence systems, which can understand, generate, and translate human languages. By analyzing large amounts of textual data, these models learn language patterns to perform tasks such as writing, conversation, and summarization. Agents built on LLMs (LLM agents) further extend these capabilities, allowing them to process user interactions and perform complex operations in diverse task environments. However, during the processing and generation of massive data, LLMs and LLM agents pose a risk of sensitive information leakage, potentially threatening data privacy. This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding. Specifically, we conduct an in-depth survey about privacy threats, encompassing passive privacy leakage and active privacy attacks. Subsequently, we introduce the privacy protection mechanisms employed by LLMs and LLM agents and provide a detailed analysis of their effectiveness. Finally, we explore the privacy protection challenges for LLMs and LLM agents as well as outline potential directions for future developments in this domain.http://www.sciencedirect.com/science/article/pii/S2667295225000042Large Language Models (LLMs)SecurityData privacyPrivacy protectionLLM agentsSurvey |
| spellingShingle | Biwei Yan Kun Li Minghui Xu Yueyan Dong Yue Zhang Zhaochun Ren Xiuzhen Cheng On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review High-Confidence Computing Large Language Models (LLMs) Security Data privacy Privacy protection LLM agents Survey |
| title | On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review |
| title_full | On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review |
| title_fullStr | On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review |
| title_full_unstemmed | On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review |
| title_short | On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review |
| title_sort | on protecting the data privacy of large language models llms and llm agents a literature review |
| topic | Large Language Models (LLMs) Security Data privacy Privacy protection LLM agents Survey |
| url | http://www.sciencedirect.com/science/article/pii/S2667295225000042 |
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