解构“数据保护-共享利用”悖论：多主体协同视角下健康医疗数据共享演化博弈研究

doi:10.13998/j.cnki.issn1002-1248.25-0519

Abstract

Abstract:

[Purpose/Significance] The governance of health medical data is fundamentally challenged by the "protection-sharing" paradox: the critical need to safeguard sensitive personal information often conflicts with the desire to utilize these data for public benefit. This issue is particularly pressing under China's "Healthy China" initiative, which promotes data sharing while the rapid expansion of medical APPs has led to increasing data misuse incidents. Existing research has extensively explored technological solutions such as blockchain, but a significant gap remains in understanding the dynamic, strategic interactions among the key stakeholders - government regulators, APP operators, and users - who operate with bounded rationality. This study addresses this gap by constructing a tripartite evolutionary game model. Its primary significance lies in dynamically modeling the co-evolution of strategies to identify critical leverage points, thereby providing a theoretical basis for designing effective collaborative governance mechanisms that can reconcile data protection with utilization and ensure the sustainable development of the health data ecosystem. [Method/Process] This study established a three-party evolutionary game model involving government regulators, medical-health APP operators, and users, based on the core assumption of bounded rationality. The model incorporated a comprehensive set of parameters, including direct benefits, various costs (compliance, regulatory), data risks, and network benefits under different regulatory scenarios. Replicator dynamic equations were derived for each party to mathematically describe the evolution of their strategy choices over time. The stability of the system's equilibrium points was rigorously analyzed using Lyapunov's first method to identify key stability thresholds. To validate the theoretical analysis and explore the dynamic evolutionary paths, numerical simulations were conducted using MATLAB. These simulations tested the impact and sensitivity of critical parameters - such as user-perceived data risk under operator self-discipline, user network benefits under dynamic regulation, government compliance rewards, and penalties for overdevelopment - from various initial strategy combinations. [Results/Conclusions] The analysis yielded several critical findings. First, users' authorization decisions are highly sensitive to the operational context, and they are significantly positively influenced by the perceived level of operator self-discipline and the observed intensity of government dynamic regulation. Enhancing user network benefits under effective regulation and reducing perceived data risks are paramount to encouraging authorization. Second, for APP operators, increasing government penalties for overdevelopment acts as a powerful deterrent, rapidly steering operators towards compliance. In contrast, government financial rewards for compliance, while effective, must be carefully balanced against their potential fiscal burden, which can slow the government's own stabilization into a dynamic regulatory role. Third, the system exhibits strong path dependence, capable of converging towards either an inefficient equilibrium (Non-Authorization, Overdevelopment, Passive Regulation) or the optimal Pareto state (Authorization, Self-discipline, Dynamic Regulation), depending heavily on initial conditions. The study concludes that resolving the paradox requires a multi-faceted strategy: advancing and ensuring robust anonymization technologies, implementing intelligent graded supervision that combines incentives and punishments, and firmly establishing institutional safeguards for user data sovereignty to build essential trust. A key limitation is the omission of data leakage risks from government data openness. Future work will integrate empirical data and consider user heterogeneity to refine the model.

Key words: health data, evolutionary game, simulation analysis, data sharing

CLC Number:

G203

LV Kun, YU Linrong, WEN Yuzhu, Li Beiwei. Deconstructing the "Data Protection-Sharing Utilization" Paradox: Research on the Evolutionary Game of Health Medical Data Sharing from a Multi-Agent Collaborative Perspective[J].Journal of library and information science in agriculture, 2026, (): 1-18.

Figures/Tables 12

Fig.1

Table 1

Model variables and their meanings"

类别	参数	含义
用户相关参数	e ₁	用户授权数据共享所获得的直接收益
	E ₁₁	政府动态监管情况下，用户所获得的网络效益
	E ₁₂	政府消极监管情况下，用户所获得的网络效益
	r ₁	医疗健康类APP运营方自律合规情况下，用户授权数据共享所面临的数据安全风险
	r ₂	医疗健康类APP运营方过度开发情况下，用户授权数据共享所面临的数据安全风险
运营方相关参数	c ₁₁	政府动态监管情况下，医疗健康类APP运营方为自律合规所投入的成本
	c ₁₂	政府消极监管情况下，医疗健康类APP运营方为自律合规所投入的成本
	e ₂	医疗健康类APP运营方因自律合规而获得的市场声誉收益
	p	医疗健康类APP运营方因过度开发行为所导致的法律纠纷及声誉损失等负效益
	E ₂₁	政府动态监管情况下，医疗健康APP营方所获得的网络效益
	E ₂₂	政府消极监管情况下，医疗健康类APP运营方所获得的网络效益
政府相关参数	c ₂₁	医疗健康类APP运营方自律合规情况下，政府实施动态监管所付出的成本
	c ₂₂	医疗健康类APP运营方过度开发情况下，政府实施动态监管所付出的成本
	c ₃	政府为激励自律合规的医疗健康类APP运营方而支付的奖励成本
	f	政府对采取过度开发策略的医疗健康类APP运营方所课收的罚款
	e ₃	政府因实施有效监管、提升公信力而获得的收益
	c ₄	政府因消极监管导致数据泄露事故后，为恢复公信力所付出的补偿成本
	E ₃₁	政府动态监管情况下，政府所获得的网络效益
	E ₃₂	政府消极监管情况下，政府所获得的网络效益
系统通用参数	ϕ	成本基础占比，用于核算无数据交互时各方承担的基础固定成本
系统通用参数	θ	用户不授权数据共享时，其间接效益的折算系数
策略比例变量	x	选择授权共享个人健康医疗数据的用户比例
	y	选择自律合规策略的医疗健康类APP运营方比例
	z	选择动态监管策略的政府比例

Table 1

Table 2

Fig.2

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Table 3

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三方博弈组合策略	用户收益	医疗健康类APP运营方收益	政府收益
（不授权数据共享，过度开发，消极监管）	E ₁₁ +e ₁ -r ₁	e ₂ -c ₁₁ +c ₃ +E ₂₁	e ₃ -c ₃ -c ₂₁ +E ₃₁
（不授权数据共享，过度开发，消极监管）	E ₁₂ +e ₁ -r ₁	e ₂ -c ₁₂ +E ₂₂	-c ₄ +E ₃₂
（不授权数据共享，过度开发，动态监管）	E ₁₁ +e ₁ -r ₂	-p-f+E ₂₁	e ₃ +f-c ₂₂ +E ₃₁
（不授权数据共享，自律合规，动态监管）	E ₁₂ +e ₁ -r ₂	-p+E ₂₂	-c ₄ +E ₃₂
（授权数据共享，过度开发，消极监管）	θE ₁₂	-ϕc ₁₁	-ϕc ₂₁
（授权数据共享，自律合规，消极监管）	θE ₁₂	-ϕc ₁₂	-c ₄
（授权数据共享，过度开发，动态监管）	θE ₁₂	0	-ϕc ₂₂
（授权数据共享，自律合规，动态监管）	θE ₁₂	0	-c ₄

均衡点	Jacobian矩阵特征值	实部符号	稳定性结论	成立条件
P₁(0,0,0)	λ1=e ₁+E ₁₂(1-θ)-r ₂ λ2=-p+E ₂₂ λ3=-c ₄+E ₃₂	(U,-,-)	潜在ESS	①
P₂(0,1,0)	λ1=e ₁+E ₁₁-θE ₁₂-r ₂ λ2=-p-f+E ₂₁ λ3=c ₄-ϕc ₂₂	(U,-,+)	不稳定	/
P₃(0,0,1)	λ1=e ₁+E ₁₂(1-θ)-r ₁ λ2=e ₂-c ₁₂+E ₂₂+ϕc ₁₂ λ3=-c ₄+E ₃₂	(U,+,-)	不稳定	/
P₄(0,1,1)	λ1=e ₁+E ₁₁-θE ₁₂-r ₁ λ2=e ₂+c ₃-c ₁₁+E ₂₁+ϕc ₁₁ λ3=c ₄-ϕc ₂₁	(U,+,+)	不稳定	/
P₅(1,0,0)	λ1=-[e ₁+E ₁₂(1-θ)-r ₂] λ2=-p+E ₂₂-ϕc ₁₂ λ3=e ₃+E ₃₁-E ₃₂+c ₄	(U,-,+)	不稳定	/
P₆(1,1,0)	λ1=-(e ₁+E ₁₁-θE ₁₂-r ₂) λ2=-p-f+E ₂₁-ϕc ₁₁ λ3=-(e ₃+f+E ₃₁-E ₃₂+c ₄-c ₂₂)	(U,-,+)	不稳定	/
P₇(1,0,1)	λ1=-(e ₁+E ₁₂(1-θ)-r ₁) λ2=-(e ₂-c ₁₂+E ₂₂+ϕc ₁₂) λ3=e ₃+E ₃₁-E ₃₂+c ₄	(U,-,+)	不稳定	/
P₈(1,1,1)	λ1=-(e ₁+E ₁₁-θE ₁₂-r ₁) λ2=-(e ₂+c ₃-c ₁₁+E ₂₁+ϕc ₁₁) λ3=-(e ₃-c ₃+E ₃₁-E ₃₂+c ₄-c ₂₁)	(U,-,-)	潜在ESS	②

Deconstructing the "Data Protection-Sharing Utilization" Paradox: Research on the Evolutionary Game of Health Medical Data Sharing from a Multi-Agent Collaborative Perspective

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