Exploring Privacy Leakage in Platform-Based Enterprises: A Tripartite Evolutionary Game Analysis and Multilateral Co-Regulation Framework

Currently, some platform-based enterprises are selling users’ private information to capture high revenue, which poses a great threat to users’ privacy and security and also poses a challenge to the work of regulators. This paper constructs a tripartite evolutionary game model among platform-based enterprises, users, and regulators by combining theories of daily activities. This paper explores the equilibrium strategy for the leakage of users’ privacy information on online platforms under the concept of multiparty governance and designs four simulation experiments based on the revenue intervals of platform-based enterprises’ decisions. Finally, the model is validated by means of simulation. The results show that when platform-based enterprises make less profit from compromising users’ privacy, users are motivated to participate in shared governance with minimal incentives from the regulator. Regulators can effectively deter privacy leakage by fining platform-based enterprises for leaking user privacy information and requiring them to compensate users for their losses. At this point, they can achieve effective control over privacy leakage by using traditional regulation. When platform-based enterprises make high profits from leaking users’ privacy, they will adhere to their privacy leaking strategy. At this point, privacy security is at its most serious, and users’ privacy rights are not effectively protected. Users and regulators will tend to cooperate to form a multiparty regulatory system, but this does not influence the strategy choice of platform-based enterprises.

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Publication Details

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Information
Published
2025-03-02
DOI
https://doi.org/10.3390/info16030193
Primary Topic
Privacy, Security, and Data Protection
Type
article
Field-Weighted Citation Impact
0.00

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article

Exploring Privacy Leakage in Platform-Based Enterprises: A Tripartite Evolutionary Game Analysis and Multilateral Co-Regulation Framework

Peng Xu, Jiaxin Li, Zhuo Sun
Information
Privacy, Security, and Data Protection
article

Exploring Privacy Leakage in Platform-Based Enterprises: A Tripartite Evolutionary Game Analysis and Multilateral Co-Regulation Framework

Peng Xu, Jiaxin Li, Zhuo Sun
article en

Abstract

Currently, some platform-based enterprises are selling users’ private information to capture high revenue, which poses a great threat to users’ privacy and security and also poses a challenge to the work of regulators. This paper constructs a tripartite evolutionary game model among platform-based enterprises, users, and regulators by combining theories of daily activities. This paper explores the equilibrium strategy for the leakage of users’ privacy information on online platforms under the concept of multiparty governance and designs four simulation experiments based on the revenue intervals of platform-based enterprises’ decisions. Finally, the model is validated by means of simulation. The results show that when platform-based enterprises make less profit from compromising users’ privacy, users are motivated to participate in shared governance with minimal incentives from the regulator. Regulators can effectively deter privacy leakage by fining platform-based enterprises for leaking user privacy information and requiring them to compensate users for their losses. At this point, they can achieve effective control over privacy leakage by using traditional regulation. When platform-based enterprises make high profits from leaking users’ privacy, they will adhere to their privacy leaking strategy. At this point, privacy security is at its most serious, and users’ privacy rights are not effectively protected. Users and regulators will tend to cooperate to form a multiparty regulatory system, but this does not influence the strategy choice of platform-based enterprises.

InformationVol. 16(3)
Zhengzhou University (CN), Jiaozuo University (CN)
China Postdoctoral Science Foundation
Openalex Percentile: Top 95%
Privacy, Security, and Data Protection
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