Electronic health record (EHR) has recorded the process of occurrence, development, and treatment of diseases. So it has high medical value. Owing to the private and sensitive nature of medical data for patients, the data sharing and privacy preservation are critical issues in EHR. Blockchain technology may be a promising solution for the problems above since it holds the features of decentralization and tamper resistance. In the paper, we propose a medical data sharing and protection scheme based on the hospital’s private blockchain to improve the electronic health system of the hospital. Firstly, the scheme can satisfy various security properties such as decentralization, openness, and tamper resistance. A reliable mechanism is created for the doctors to store medical data or access the historical data of patients while meeting privacy preservation. Furthermore, a symptoms-matching mechanism is given between patients. It allows patients who get the same symptoms to conduct mutual authentication and create a session key for their future communication about the illness. The proposed scheme is implemented by using PBC and OpenSSL libraries. Finally, the security and performance evaluation of the proposed scheme is given.

Data security is becoming increasingly critical as outsourced data services flourish. An effective solution for ensuring data confidentiality in the cloud is attribute-based searchable encryption (ABSE). However, most extant ABSE solutions have not been able to simultaneously achieve rich expressive queries, result validation, attribute revocation, and policy hiding, and there are problems such as inflexibility in query methods and access control capabilities, insufficient scalability and practicality, and privacy leakage. In particular, it should be noted that most ABSE schemes fail to promise the privacy security of access policies and are susceptible to offline keyword guessing attack. Hidden policy-ABSE (HP-ABSE) has been proposed to tackle the issue of explicit attribute values in access policies potentially compromising privacy. Nevertheless, in most existing HP-ABSE schemes, an adversary may initiate attribute values guessing attack to speculate on the values of attribute within access strategy. To this end, this paper proposes partially hidden policy attribute-based multi-keyword searchable encryption with verification and revocation (PHP-ABMKSE-VR). PHP-ABMKSE-VR scheme is practical, secure, as well as effective, making it appropriate for real-world scenarios like smart healthcare, as demonstrated by the experimental outcomes and safety analysis.

With the rapid development of digital communication, cloud computing, and intelligent applications such as smart healthcare and the Internet of Things (IoT), secure image transmission has become a critical challenge. Traditional encryption algorithms often face limitations including narrow key spaces, insufficient sensitivity to initial parameters, and weak resistance to statistical and differential attacks, leaving encrypted images vulnerable to security breaches. To address this gap, we propose a novel image encryption algorithm that integrates a three-dimensional Chebyshev-Henon hyperchaotic map (3D-CHM), Josephus permutation, wavelet transform, and a Gaussian noise-driven diffusion mechanism. First, the 3D-CHM generates complex, unpredictable chaotic sequences with high sensitivity to initial conditions, providing a substantially larger key space. Second, a pixel substitution strategy combining Josephus permutation and chaotic sequences ensures global pixel shuffling and effectively reduces inter-pixel correlation. Third, a dynamic diffusion process incorporating Gaussian noise further enhances robustness against various attacks. Experimental results demonstrate that the proposed algorithm achieves a vast key space of approximately \(2^{256}\), strong resistance against brute-force, statistical, and differential attacks, and excellent performance metrics (Number of Pixels Change Rate (NPCR) = 99.609463%, Uniform Average Change Intensity (UACI) = 33.462821%). Comparative evaluations with state-of-the-art schemes confirm the superior security, efficiency, and robustness of our method across images of different resolutions. These results highlight the potential of the proposed scheme for practical deployment in privacy-critical scenarios such as medical image protection and IoT-based visual data security.