The healthcare Internet of Things (IoT), which relies on wearable devices to collect patient health data and aggregate it on cloud server, plays a key role in modern healthcare system. How to achieve bidirectional access control while ensuring secure information transmission is a significant challenge facing the healthcare industry. Identity-based matchmaking encryption (IB-ME) and attribute-based matchmaking encryption (AB-ME) show great research potential in achieving these functions. However, IB-ME schemes are typically limited to one-to-one scenarios. While AB-ME enables flexible many-to-many communication, its high communication overhead made it difficult for direct application in resource-constrained environments. Therefore, striking a balance between functionality and efficiency is crucial. Moreover, in healthcare IoT system, user deletion or revocation of decryption permission may be necessary due to the possibility of malicious behavior, organization changes, or discontinuing subscription services. To address these issues, this paper proposes an efficient revocable attribute-based matchmaking encryption (RAB-ME) scheme for healthcare IoT systems. This scheme not only supports bilateral access control but also enables user permission revocation while maintaining high efficiency. We analyzed the privacy and authenticity of this scheme based on the random oracle model. Through extensive comparisons and experimental simulations, our scheme outperforms existing approaches.

In the coming 6G area, the throughput burden of cellular networks is generated by the the explosive growth in the number of smart devices and its requirements. The throughput burden can be mitigated effectively by the Device-to-Device Mul-ticast (D2MD) communication technology-based content sharing. With the aim of gaining the benefit of content sharing of D2MD, it is necessary to adopt an appropriate method for cluster formation that considers adaptivity and comprehensive influence. Thus, to address this method, this paper designs a global rate maximization problem by comprehensively taking into account the impact factors of the physical and social layers. Due to the high complexity of the designed problem, an Evolution-Affinity Propagation (EAP) algorithm is proposed to solve it. Finally, the performance of EAP is verified in simulation results and shows that the performance of EAP is improved by 2.5 times compared with spectral clustering.

Digital entrepreneurial success has gained attention because the development of digitalization has brought multiple opportunities for entrepreneurs along with many challenges. While prior research has primarily examined the determinants of success at the founder and organizational levels, the impact of venture groups has been neglected, resulting in a lack of exploration of the group effect. In this study, we apply social learning theory to investigate how lead entrepreneur's digital leadership influences digital entrepreneurial success. By analyzing data from 88 digital entrepreneurial teams, we found that lead entrepreneur's digital leadership is positively related to entrepreneurial team's digital entrepreneurial success. Likewise, lead entrepreneur's digital leadership positively correlates with digital entrepreneurial success through technology absorptive capacity and technological innovation capability. Moreover, we argue that leader group prototypicality not only strengthens the impact of lead entrepreneur's digital leadership on the entrepreneurial team's technology absorptive capacity but also amplifies its influence on the entrepreneurial team's technological innovation capability. Our results provide contributions to both the entrepreneurship and leadership literature.