With the rapid development of data technologies, the high privacy sensitivity of big data in healthcare imposes higher demands on its security supervision. This paper analyzes the interactive dynamics between the behaviors of regulators and regulated entities, aiming to explore the symmetry and balance between the utilization and protection of big data in healthcare in China. A two-party evolutionary game model between regulators and regulated entities is constructed and refined by incorporating herding preference utility coefficients, and simulation analyses are performed using MATLAB. Furthermore, the main models and differences in health data regulation among the United States, the European Union, the United Kingdom, and China are discussed for broader relevance. This study finds that the fine amount imposed on regulated entities during process supervision has a significant impact on their behavior, yet it cannot eliminate unstable fluctuations in the system. Reducing the prevention costs of regulated entities is the fundamental approach for the system to achieve an equilibrium state of maximum social welfare. Herding preference utility enhances system stability, and when this utility is sufficiently strong, it may even eliminate unstable fluctuations in the system. It is suggested that regulators should carefully consider the prevention costs of regulated entities when proposing prevention requirements, implement subsidy policies when necessary, explore a new model of multi-stakeholder collaborative supervision, enhance the risk awareness of relevant organizations, and strengthen publicity and guidance, thereby achieving the goal of big data security supervision in healthcare.

Amid rapid digital transformation and accelerated AI advancements, healthcare data utilization has gained global prominence. Empirical evidence demonstrates that public trust is pivotal to sustainable healthcare data sharing, with transparency being its key determinant. This study investigates the transparency‐trust relationship through evolutionary game theory. A tripartite stochastic evolutionary game model is developed, incorporating Gaussian white noise to capture real‐world complexity. Through analyzing interactions among data platforms, users, and patients, four key findings emerge: (1) Enhancing the regulatory efficiency and penalty intensity of data platforms can drive the system towards an effective equilibrium. (2) Both external regulatory pressure and long‐term trust benefits are influential factors that encourage data users to choose TU. (3) The dynamic feedback mechanism of “risk speculation—patient withdrawal—strategy shift—system equilibrium” serves as the intrinsic driving force for the system’s self‐repair and stability. (4) The intrinsic motivation for patients to choose participation is multidimensional; both privacy and security protection and the benefits derived from data sharing are influential factors driving patients to adopt PA.

In the circulation of health data, the mutual trust dilemma leads to the symmetric problems of “inadequate supply” on the supply side and “impeded flow” on the demand side, which hinder the realization of data value. This paper analyzes the formation mechanism of this dilemma and its resolution pathways from a symmetry perspective. First, based on a literature review, we extract the manifestations of the mutual trust dilemma and construct a two-stage decision-making “vicious circle” model of mutual trust. Unlike conventional single-stage game models, this framework captures the sequential nature of trust decisions—entry in Stage 1 and compliance in Stage 2. Second, we establish a tripartite evolutionary game model involving data suppliers, demanders, and regulators, and employ MATLAB simulations to examine the impact of key parameters on strategic evolution. The findings reveal that: (1) beneficial data utilization scenarios are the prerequisite for participation; (2) strong regulation is the fundamental guarantee for mutual trust; and (3) while increasing penalties, reducing compliance costs, and enhancing trust-related gains can promote compliance, regulatory measures must be carefully balanced to avoid suppressing participation willingness. Accordingly, we propose a four-step progressive mutual trust mechanism comprising motivation activation, cooperation facilitation, performance guarantee, and trust reinforcement. Policy recommendations are offered regarding high-benefit scenarios, trusted data spaces, incentive policies, and data standardization, with the aim of building a trusted health data circulation ecosystem.