Secure E-health Framework in Cloud-Based Environment

Since December 2019, the world still fighting to beat coronavirus (COVID-19). However, coronavirus is continuing its spread in many countries and claimed the lives people. It is not easy to differentiate between COVID-19 symptoms and simple flu symptoms, especially at the first stage of the infection. This is the main challenge where we have to run many tests as possible and isolate any suspicious people 14 days at least to make sure that they are not carrying the virus. This will increase the cost and people may lose their jobs. Therefore, the economy has to continue. Companies and organization start running their business using online tools, this will draw different future and employee need to gain special task to continue their work. In order go back to the normal life, we have track the virus and stay away from infected area or people. In this paper, we propose a secure cloud-based health framework to record patients' readings, give initial diagnose to identify infected areas and control the spread of the virus. The proposed framework will be running in a secure environment to protect patient's records. © 2022 IEEE.

Secret sharing based watermarking for copy-protection and ownership control of medical Image

Medical data transmission and sharing, especially during this COVID-19 pandemic period, on the open channel have become more important for remote diagnosis and treatment purpose. However, the alteration and unauthorized distribution of image data has become easier, and thus the big issue of copy-protection and ownership conflicts has attracted more attention for healthcare research community. Further, large amount of confidential and personal medical records is often stored on cloud environments. However, outsourcing medical data possibly brings the great security and privacy issue, since the confidential records are shared to the third party. In this paper, a robust X-Ray image watermarking is proposed by using Non-Subsampled Contourlet Transform (NSCT) and Multiresolution Singular Value Decomposition (MSVD). For watermark embedding, the maximum entropy component of X-Ray carrier image is firstly decomposed using NSCT. Then, low and high frequency details of carrier and mark image is obtained using MSVD. Further, conceal the watermark detail through modifying the detail of carrier image via the suitable factor. Finally, Shamir's (k, n) secret sharing algorithm is employed to obtain secure marked carrier image. Objective evaluations on 200 X-Ray images of COVID-19 patients demonstrate that the proposed algorithm has not only an excellent invisibility but a strong robustness against the various attacks. The results also show that our algorithm outperforms the related image watermarking algorithms, since it is also suitable for applications in the multi-cloud. © 2021 IEEE.