To address the challenges of weak identity binding between sensors and users, as well as insufficient data security in the Internet of Medical Things (IoMT), this paper proposes a blockchain-based certificateless aggregate signcryption scheme. The scheme integrates dynamic anonymous identity generation with aggregate signcryption techniques to achieve lightweight encryption while enhancing protocol-level identity binding between sensors and device users. By storing anonymous identities, public keys, and signcrypted data on the blockchain, the proposed approach enables verifiable identity auditing and tamper-proof data preservation, thereby improving the overall trustworthiness and traceability of the system. Under the random oracle model, we formally prove the confidentiality and unforgeability of the scheme against both external and internal adversaries. Experimental evaluations are conducted in the Hyperledger Fabric environment and on an NVIDIA Jetson Nano Developer Kit, demonstrating the proposed scheme’s superior performance in computation efficiency, communication overhead, and practical deployability.

Federated learning (FL) is a new decentralized deep learning paradigm developed for collaborative model training and solving the problem of data privacy and has received extensive attention from both the academic and business worlds. However, FL still faces challenges in encouraging participants to contribute private data and computational resources. Although many studies have applied game theory models to improve the incentive mechanism design of FL, they assume that the players are absolutely rational and that the game models are static. In this study, a mathematical model based on evolutionary game theory (EGT) is established to analyze the interaction between parameter servers and participants, considering that the participants are not completely rational in the long-term dynamic decision-making process. The evolutionarily stable status of the FL system and the strategies of the parameter servers and participants were analyzed under eight different scenarios. Based on the model analysis and results of the numerical experiments, managerial insights for maintaining a sustainable FL system are summarized.

To provide high-quality service, it is necessary to emphasize customers’ emotional needs. It is necessary to determine the priority strategy for service attributes owing to limited resources and different relative levels of influence on customer perception. Therefore, this study proposed a service design framework based on the Kansei Engineering (KE) and Kano models to design services oriented toward customer perception. In the modeling analysis stage, the partial least squares algorithm and decision tree mining were used to construct a relationship model between service perception and attribute, and use the intention model. Finally, the in-flight service of a Chinese airline was considered as an application case study. The results obtained thus revealed a relationship between perception and service attributes, and their influence on customers’ intentions. These validated the proposed method and provided new concepts for service design.