Evolutionary game analysis of stakeholder privacy management in the AIGC model
The technological development powered by Artificial Intelligence Generated Content (AIGC) models, exemplified by Generative Pre-trained Transformer 4 (GPT-4) and Bidirectional Encoder Representations from Transformers (BERT), has completely transformed machine language processing and fostered substa...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Operations Research Perspectives |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221471602500003X |
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| author | Yali Lv Jian Yang Xiaoning Sun Huafei Wu |
| author_facet | Yali Lv Jian Yang Xiaoning Sun Huafei Wu |
| author_sort | Yali Lv |
| collection | DOAJ |
| description | The technological development powered by Artificial Intelligence Generated Content (AIGC) models, exemplified by Generative Pre-trained Transformer 4 (GPT-4) and Bidirectional Encoder Representations from Transformers (BERT), has completely transformed machine language processing and fostered substantial technological advancements. However, their extensive deployment has amplified concerns regarding data privacy risks, which are attributed not only to technological vulnerabilities but also to the intricate conflicts of interest among model providers, application service providers, and privacy regulators. To tackle this challenge, this research develops a tripartite evolutionary game model that examines the strategic interactions and dynamic relationships among large language model providers, application service providers, and privacy regulatory agencies. By employing replicator dynamic equations and Jacobian matrices, the research investigates the stability of strategic equilibria and simulates optimal adjustment paths across diverse policy scenarios. Drawing on the research findings, this paper offers practical recommendations to strengthen data privacy protection in large language models, delivering a solid theoretical foundation for policymakers and industry practitioners. |
| format | Article |
| id | doaj-art-53d7d45b2fc1449d8a2b36e6e393e1bf |
| institution | Kabale University |
| issn | 2214-7160 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Operations Research Perspectives |
| spelling | doaj-art-53d7d45b2fc1449d8a2b36e6e393e1bf2025-08-20T03:47:13ZengElsevierOperations Research Perspectives2214-71602025-06-011410032710.1016/j.orp.2025.100327Evolutionary game analysis of stakeholder privacy management in the AIGC modelYali Lv0Jian Yang1Xiaoning Sun2Huafei Wu3School of Information, Shanxi University of Finance and Economics, Taiyuan, 030006, Shanxi, ChinaCorresponding author.; School of Information, Shanxi University of Finance and Economics, Taiyuan, 030006, Shanxi, ChinaSchool of Information, Shanxi University of Finance and Economics, Taiyuan, 030006, Shanxi, ChinaSchool of Information, Shanxi University of Finance and Economics, Taiyuan, 030006, Shanxi, ChinaThe technological development powered by Artificial Intelligence Generated Content (AIGC) models, exemplified by Generative Pre-trained Transformer 4 (GPT-4) and Bidirectional Encoder Representations from Transformers (BERT), has completely transformed machine language processing and fostered substantial technological advancements. However, their extensive deployment has amplified concerns regarding data privacy risks, which are attributed not only to technological vulnerabilities but also to the intricate conflicts of interest among model providers, application service providers, and privacy regulators. To tackle this challenge, this research develops a tripartite evolutionary game model that examines the strategic interactions and dynamic relationships among large language model providers, application service providers, and privacy regulatory agencies. By employing replicator dynamic equations and Jacobian matrices, the research investigates the stability of strategic equilibria and simulates optimal adjustment paths across diverse policy scenarios. Drawing on the research findings, this paper offers practical recommendations to strengthen data privacy protection in large language models, delivering a solid theoretical foundation for policymakers and industry practitioners.http://www.sciencedirect.com/science/article/pii/S221471602500003XAIGCData privacyEvolutionary gameReplicator dynamic equations |
| spellingShingle | Yali Lv Jian Yang Xiaoning Sun Huafei Wu Evolutionary game analysis of stakeholder privacy management in the AIGC model Operations Research Perspectives AIGC Data privacy Evolutionary game Replicator dynamic equations |
| title | Evolutionary game analysis of stakeholder privacy management in the AIGC model |
| title_full | Evolutionary game analysis of stakeholder privacy management in the AIGC model |
| title_fullStr | Evolutionary game analysis of stakeholder privacy management in the AIGC model |
| title_full_unstemmed | Evolutionary game analysis of stakeholder privacy management in the AIGC model |
| title_short | Evolutionary game analysis of stakeholder privacy management in the AIGC model |
| title_sort | evolutionary game analysis of stakeholder privacy management in the aigc model |
| topic | AIGC Data privacy Evolutionary game Replicator dynamic equations |
| url | http://www.sciencedirect.com/science/article/pii/S221471602500003X |
| work_keys_str_mv | AT yalilv evolutionarygameanalysisofstakeholderprivacymanagementintheaigcmodel AT jianyang evolutionarygameanalysisofstakeholderprivacymanagementintheaigcmodel AT xiaoningsun evolutionarygameanalysisofstakeholderprivacymanagementintheaigcmodel AT huafeiwu evolutionarygameanalysisofstakeholderprivacymanagementintheaigcmodel |