In the digital era, secure data sharing has become a core requirement for enabling cross-domain collaboration, cloud computing, and Internet of Things (IoT) applications, as well as a critical measure for safeguarding privacy and defending against malicious attacks. In light of the risks of data leakage and misuse in open environments, achieving efficient, controllable, and privacy-preserving data sharing has emerged as a key research focus. This paper first provides a systematic review of the prevailing secure data sharing technologies, including proxy re-encryption, searchable encryption, key agreement and distribution, and attribute-based encryption, summarizing their design principles and application features. Subsequently, game-theoretic modeling based on incentive theory is introduced to construct a strategic interaction framework between data owners and data users, aiming to analyze and optimize benefit allocation mechanisms. Furthermore, the paper explores the integration of game theory with secure sharing mechanisms to enhance the sustainability and stability of the data sharing ecosystem. Finally, it outlines the critical challenges currently faced in secure data sharing and discusses future research directions, offering theoretical insights and technical references for building a more comprehensive data sharing framework.

In the context of the rapid growth of the Internet of Things and mobile health services, sensors and smart wearable devices are continuously collecting and uploading dynamic health data. Together with the long-term accumulated electronic medical records and multi-source heterogeneous clinical data from healthcare institutions, these data form the cornerstone of intelligent healthcare. In the context of medical data sharing, previous studies have mainly focused on privacy protection and secure data transmission, while relatively few have addressed the issue of incentive mechanisms. However, relying solely on technical means is insufficient to solve the problem of individuals’ willingness to share their data. To address this challenge, this paper proposes a three-party Stackelberg-game-based incentive mechanism for medical data sharing. The mechanism captures the hierarchical interactions among the intermediator, electronic device users, and data consumers. In this framework, the intermediator acts as the leader, setting the transaction fee; electronic device users serve as the first-level followers, determining the data price; and data consumers function as the second-level followers, deciding on the purchase volume. A social network externality is incorporated into the model to reflect the diffusion effect of data demand, and the optimal strategies and system equilibrium are derived through backward induction. Theoretical analysis and numerical experiments demonstrate that the proposed mechanism effectively enhances users’ willingness to share data and improves the overall system utility, achieving a balanced benefit among the cloud platform, electronic device users, and data consumers. This study not only enriches the game-theoretic modeling approaches to medical data sharing but also provides practical insights for designing incentive mechanisms in IoT-based healthcare systems.

With the rapid development of quantum computing, in response to its disruptive threats to traditional cryptosystems and the urgent demand for lightweight and highly scalable secure group communication among resource-constrained devices in large-scale Internet of Things (IoT) scenarios, this paper proposes a lightweight anonymous group authentication scheme that integrates Physical Unclonable Functions (PUFs), distributed Gossip algorithms, and quantum key distribution. By exploiting the uniqueness and unclonability derived from the inherent physical characteristics of PUF hardware, the scheme fundamentally eliminates attack vectors against quantum computers without requiring devices to pre-store any secret keys in their memory, while the QKCS pre-provisions CRPs and key seeds, which is the standard enrollment procedure in PUF-based systems. Combined with information-theoretically secure quantum keys as session keys, it forms a dual protection mechanism: anti-forgery at the physical layer and anti-quantum attack at the cryptographic layer. Innovatively, the Gossip algorithm is deeply integrated with group key agreement, converting global broadcast into local iterative interactions between nodes, which effectively alleviates broadcast storms and improves the scalability and fault tolerance of the protocol. Meanwhile, a pseudonym mechanism is introduced to achieve anonymous identity protection, and a dynamic key update strategy guarantees forward and backward security when members join or leave the group. Formal verification based on BAN logic and security analysis show that the proposed protocol can resist typical attacks such as replay attacks, man-in-the-middle attacks, and impersonation attacks. Performance evaluations demonstrate that our scheme outperforms existing comparable schemes in terms of computational cost, communication overhead, and dynamic group management efficiency, demonstrating its potential for resource-constrained IoT environments, pending further validation on real hardware platforms.