Lamri Laouamer

Medical data protection against illegal manipulations has become an essential and urgent issue. Unfortunately, images exchanged via networks are not absolutely protected against the preservation of integrity, authenticity and the right of use. Watermarking can play a very important role in dealing with this problem. For this reason, it becomes necessary to perform such watermarking in image regions where the disorder regarding the pixels distribution should be less when embedding secret data called watermark. In this paper, we propose a new approach of medical image watermarking in spatial domain with a non-blind way. This approach is based on one of the essential properties of image called Local Entropy (LE). The watermarking in the zones with less local entropy values guarantees a high imperceptibility of the watermark. The choice of the low local entropy is based on measuring or estimating changes in a zones or regions of the image. The watermark embedding consists only on pixels with less local entropy values since these pixels represent less disorder within the image. The results obtained are very encouraging and have been evaluated in terms of imperceptibility through the Peak Signal Noise Rate (PSNR) metric and evaluated also in terms of robustness by measuring the Correlation Coefficients (CC), Bit Error Ratio (BER) and Structural Similarity Index Measure (SSIM) between the original watermark and the extracted ones.

Medical image watermarking represents a promising alternative tool regarding security, digital rights, authenticity and integrity issues. In this paper, an informed medical image watermarking scheme is proposed based on local binary pattern LBP. A watermark is built based on the significant information extracted from the host image by LBP and will be addressed to be embedded through a linear interpolation. Scenarios of geometric and non-geometric attacks have been realized on the watermarked images to evaluate the robustness of the embedded watermark in the extraction process. Furthermore, it is verified through achieved experiments that the proposed scheme is imperceptible and more robust from the achieved results which are very encouraging.

Nowadays, advancements in health technology have characterized modern illness diagnosis methods in the global healthcare system. The World Health Organization (WHO) has received a large number of respiratory disease patients in recent years, resulting in billions of personal health records (PHRs) being created and transmitted via cloud with the aim of taking precautions and restricting the disease. The transmission of PHR through Internet has generated significant security and privacy concerns for the global health care system. Furthermore, the potential change in the patient's health situation at any time needs immediate and secure healthcare access. For robust authentication and optimal confidentiality, it is necessary to reinforce the access control policies. As a result, various security schemes have been developed but lack efficiency while deploying cloud‐based e‐health applications, where the cloud providers are honest but curious and may obtain sensitive data without the consent of users. In this article, we present a new security scheme for e‐health mobile applications based on hybrid cipher text‐policy attribute access control, blockchain, and context‐awareness (Cx‐CP‐ABE). The approach is based on reinforcing security policies pertaining to the continuous evolution of the patient's state and context changes and the improvement of the patient's data confidentiality. The proposed security scheme has been evaluated under various patient contexts, and its performance has been measured in terms of sensitivity, number of health attributes, and execution time.