The marketization of data elements is a critical step in harnessing the potential of data as a “new” economic element. This process is essential for driving the digital transformation of manufacturing enterprises and achieving sustainable development. Using data from listed manufacturing enterprises (2013–2022), this study applied the difference-in-differences method to analyze how the marketization of data elements accelerates digital transformation. The results indicated that data trading platforms play a significant role by lowering enterprise costs, easing financing constraints, fostering digital technology innovation, and promoting the growth of the digital service industry. The impact varies across regions, ownership structures, and executive backgrounds. This research offers fresh insights into facilitating the digital transformation of manufacturing enterprises and provides practical guidance for policymakers to advance the market-oriented reform of data elements and enhance enterprises’ digital capabilities.

The realization of the value of health big data relies on the coordinated cooperation among patients, the government, and data users. Enhancing the symmetry and balance between patient participation and the compliant use of data by data users is a critical link. This paper constructs a tripartite evolutionary game model and employs MATLAB R2023a simulation to analyze the impact of factors such as initial willingness, compliance costs, and penalties for violations on the strategic choices of the game players and the evolution of the system. The findings reveal that: (1) Patient participation is a key condition for achieving an ideal equilibrium in the system. (2) The data service income from participating in data provision and the costs associated with privacy breaches are critical factors influencing patients’ strategic choices. (3) Penalties for violations are a crucial factor in ensuring that data users choose compliant utilization; however, when compliance costs are high, their constraining effect may be somewhat diminished. (4) Enhancing regulatory efficiency is the future direction for government departments. Based on these findings, countermeasures and suggestions are proposed, including trust building, technological innovation and differentiated supervision, and constructing trusted data spaces, to provide references for health big data governance.

In the digital economy, information asymmetry among individuals, data users, and governments limits the full realization of personal data value. To address this, “symmetry enhancement” strategies aim to reduce information gaps, enabling more balanced decision-making and facilitating efficient data flow. This study establishes a tripartite evolutionary game model based on personal data collection and development, conducts simulations using MATLAB R2024a, and proposes countermeasures based on equilibrium analysis and simulation results. The results highlight that individual participation is pivotal, influenced by perceived benefits, management costs, and privacy risks. Meanwhile, data users’ compliance hinges on economic incentives and regulatory burdens, with excessive costs potentially discouraging adherence. Governments must carefully weigh social benefits against regulatory expenditures. Based on these findings, this paper proposes the following recommendations: use personal data application scenarios as a guide, rely on the construction of personal trustworthy data spaces, explore and improve personal data revenue distribution mechanisms, strengthen the management of data users, and promote the maximization of personal data value through multi-party collaborative ecological incentives.

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.

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.