Work

Evolutionary game analysis of stakeholder privacy management in the AIGC model

Year: 2025

Type: article

Abstract: 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.

Source: Operations Research Perspectives

DOI: 10.1016/j.orp.2025.100327

Authors: Yali Lv, Jian Yang, Xiaoning Sun, Huafei Wu

Institution: Shanxi University of Finance and Economics

Language: English

Topic: Privacy, Security, and Data Protection

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,790