To address the challenges of value distribution in multi-agent data sharing within digital innovation networks, this study proposes a fair and efficient allocation mechanism designed to optimize data resource flows and incentivize active participation among innovation actors. A comprehensive framework is constructed by integrating agent-based modeling (ABM) with complex network theory to capture the interactive dynamics among heterogeneous agents. The classical Susceptible-Infected-Removed (SIR) model is extended through the addition of a secondary diffusion layer to simulate both behavioral evolution and data-sharing propagation. Distributional fairness is quantitatively evaluated using the Lorenz curve and Theil index. Simulation results indicate that the incentive coefficient substantially enhances profit growth and adoption speed, while the profit-sharing coefficient critically shapes income distribution between platforms and innovators. The cost coefficient exhibits a nonlinear effect: moderate levels improve system efficiency, whereas excessive cost burdens diminish returns. Multiple linear regression analysis confirms the robustness of the model, and statistical significance tests validate the reliability of the results. These findings demonstrate the model’s effectiveness in balancing incentives and efficiency across heterogeneous stakeholders, providing both theoretical insights and practical guidance for strategic decision-making in the digital economy.

With the characteristics of free installation and rich functionalities, mobile mini-apps have become more and more popular in people’s daily life. A large amount of sensitive personal data is thus involved in them and shared across users for providing various services, which raises great privacy concerns. However, few researchers have paid attention to the potential privacy risks that may exist when user data is shared across users in mobile mini-apps. In this paper, we introduce a novel privacy risk that is brought forward by cross-user personal data over-delivery (denoted as XPO) in mobile mini-apps. Such a discovered privacy risk is demonstrated to be able to cause serious leakage of diverse user data. To detect XPO risk, a dynamic and lightweight mini-app analysis framework – XPOScope is proposed. XPOScope is able to automatically identify XPO risk at a large scale. By applying it to 4,273 mini-apps hosted on three popular platforms, i.e., WeChat, Baidu and Alipay, XPOScope reported 71 vulnerable ones, with a precision of 92.21% and a recall of 80.68%. In addition to the mere exposure of diverse private user data, case studies performed show that XPO in mini-apps can further lead to impersonation attacks, the infringement of employees’ privacy, economic loss and even the leakage of sensitive business secrets. The results call for the awareness and actions of mobile mini-app developers to secure cross-user personal data delivery. The code of this work is available at https://github.com/ppflower/XPOScope.