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• W4386005910 abstract "Currently, data transmission is an expanding area in healthcare, enabling health practitioners to examine, assess, and manage patients using mobile communication technologies. To identify and analyze a patient, healthcare providers need to access the physician's Electronic Medical Record (EMR), which may contain extensive audiovisual big data such as MRIs, CT scans, PET scans, X-rays, and more. To ensure accessibility and scalability for healthcare workers and consumers, the EMR needs to be stored in large data repositories on cloud servers. However, due to the sensitive nature of medical information stored in the cloud, the healthcare profession faces numerous security challenges, with data theft attacks being one of the most critical vulnerabilities. This research focuses on protecting medically sensitive data in the cloud by leveraging cloud computing facilities. The upgraded AES approach ensures that confidential data is securely accessible and stored. In addition, improved Elliptic Curve Cryptography (ECC) is utilized for key generation and validation. A hybrid optimization approach, combining robust optimization and genetic algorithms, is employed to select unique and distinct keys. Decryption is performed using deep neural networks, and Convolutional Neural Networks (CNN) enable batch encryption of multiple documents. The comparison between old methods and the proposed approach is based on encryption time, decryption time, and security strength." @default.
• W4386005910 created "2023-08-20" @default.
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• W4386005910 date "2023-10-01" @default.
• W4386005910 modified "2023-10-12" @default.
• W4386005910 title "Secure Authentication and Data Transmission for Patients Healthcare Data in Internet of Medical Things" @default.
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• W4386005910 doi "https://doi.org/10.33889/ijmems.2023.8.5.058" @default.
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