Privacy risks induced by generative large language models and governance paths

Large language models(LLM) are leading to a new revolution in artificial intelligence technology. As LLM have the characteristics of platform, mass data dependence, frequent user interaction, and easy to be attacked, the risk of privacy disclosure is more worrying. The source and internal mechanism...

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Main Authors: LI Yaling, CAI Jingjing, BAI Jieming
Format: Article
Language:zho
Published: POSTS&TELECOM PRESS Co., LTD 2024-09-01
Series:智能科学与技术学报
Subjects:
Online Access:http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-0271.202431
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author LI Yaling
CAI Jingjing
BAI Jieming
author_facet LI Yaling
CAI Jingjing
BAI Jieming
author_sort LI Yaling
collection DOAJ
description Large language models(LLM) are leading to a new revolution in artificial intelligence technology. As LLM have the characteristics of platform, mass data dependence, frequent user interaction, and easy to be attacked, the risk of privacy disclosure is more worrying. The source and internal mechanism of privacy leakage caused by LLM were focused. After reviewing the domestic and foreign experience in the governance of LLM privacy risks, a five-dimensional governance framework that included policies, standards, data, technology, and ecology were proposed. Finally, this paper also looks forward to the new privacy disclosure risks that LLM may face under the development trend of multimodal, agent, embodied intelligence, edge intelligence, etc.
format Article
id doaj-art-c29b3e5fd4024b0d8b76df144b79afcd
institution Kabale University
issn 2096-6652
language zho
publishDate 2024-09-01
publisher POSTS&TELECOM PRESS Co., LTD
record_format Article
series 智能科学与技术学报
spelling doaj-art-c29b3e5fd4024b0d8b76df144b79afcd2024-11-11T06:54:08ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522024-09-01639440176168136Privacy risks induced by generative large language models and governance pathsLI YalingCAI JingjingBAI JiemingLarge language models(LLM) are leading to a new revolution in artificial intelligence technology. As LLM have the characteristics of platform, mass data dependence, frequent user interaction, and easy to be attacked, the risk of privacy disclosure is more worrying. The source and internal mechanism of privacy leakage caused by LLM were focused. After reviewing the domestic and foreign experience in the governance of LLM privacy risks, a five-dimensional governance framework that included policies, standards, data, technology, and ecology were proposed. Finally, this paper also looks forward to the new privacy disclosure risks that LLM may face under the development trend of multimodal, agent, embodied intelligence, edge intelligence, etc.http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-0271.202431large language model;privacy;data leakage;government
spellingShingle LI Yaling
CAI Jingjing
BAI Jieming
Privacy risks induced by generative large language models and governance paths
智能科学与技术学报
large language model;privacy;data leakage;government
title Privacy risks induced by generative large language models and governance paths
title_full Privacy risks induced by generative large language models and governance paths
title_fullStr Privacy risks induced by generative large language models and governance paths
title_full_unstemmed Privacy risks induced by generative large language models and governance paths
title_short Privacy risks induced by generative large language models and governance paths
title_sort privacy risks induced by generative large language models and governance paths
topic large language model;privacy;data leakage;government
url http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-0271.202431
work_keys_str_mv AT liyaling privacyrisksinducedbygenerativelargelanguagemodelsandgovernancepaths
AT caijingjing privacyrisksinducedbygenerativelargelanguagemodelsandgovernancepaths
AT baijieming privacyrisksinducedbygenerativelargelanguagemodelsandgovernancepaths