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.

As the medical information systems continue to develop, the sharing of electronic medical records (EMRs) is becoming a vital tool for improving the quality and efficiency of medical services. However, during the process of sharing EMRs, establishing mutual-trust relationships and increasing users’ participation are urgent problems to be solved. Current solutions mainly focus on incentive mechanisms for users’ honest and active participation, but often ignore the potential impact of research institutions’ behavior on users’ trust and participation. To address this, this paper proposes an incentive mechanism based on evolutionary game theory. It combines the unchangeable nature of blockchain and the dynamic adjustment characteristics of evolutionary games to build a secure and trustworthy incentive system. This system considers the potential malicious behaviors of both users and research institutions, encouraging research institutions to protect users’ privacy, reduce users’ concerns, and guide users to actively contribute data. At the same time, it ensures data security and system trust through clear rewards and punishments. Based on this, we have carried out a comprehensive simulation using game theory. The results confirm that our designed incentive mechanism can effectively achieve its expected goals.

Computational ghost imaging (CGI) is an imaging technique that uses the second-order correlation properties of optical fields. It uses random illumination patterns to illuminate the object, collects the signal via a single-pixel bucket detector, and later reconstructs an image of the object. By treating the illumination patterns as keys and the bucket measurements as ciphertexts, CGI can be regarded as an encryption process. This paper proposes a multi-party collaborative decryption scheme based on CGI, which aims to reduce key transmission data whilst enabling cooperative decryption among multiple users and verifying the legitimacy of the ciphertext source via digital watermarking. This method uses a chaotic system to generate a measurement matrix, and only the chaotic parameter needs transmission. The receiver can reconstruct the measurement matrix, thereby reducing the amount of key transmission. Further, verifiable secret sharing (VSS) is employed to divide the chaotic parameter into multiple shares, which are then distributed to different recipients. Decryption is only feasible when a threshold number of recipients collaborate to reconstruct the chaotic parameter. To further improve security, hashed identity information is embedded into the ciphertext as a watermark, enabling confirmation of the ciphertext’s origin. Experimental results show the feasibility of the proposed scheme and its ability to resist tampering, offering a new approach to secure multi-user communication.

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.