Patient's data privacy protection in medical healthcare transmission services using back propagation learning. (September 2022) - Altameem, Ahmed; Kovtun, Viacheslav; Al-Ma'aitah, Mohammed; Altameem, Torki; H, Fouad; Youssef, Ahmed E.
Highlights: It introduces Healthcare Data Privacy to improve privacy maintenance. It identifies the need for encrypting and decrypting the accumulated healthcare data. It operates in two levels for verifying the security measures to prevent data losses. Abstract: Medical healthcare services rely on communication technologies for exchanging digital records of patients. The new digitalizing of health records brings a specific change in healthcare services. The Electronic Health Records (EHRs) contains cumulative information about patients, such as medical history, observations, diagnostics, specimens, and reports. EHRs are sensitive information readily available for patient's and healthcare providers' access while maintaining privacy. Therefore, preserving security and privacy is of utmost importance for healthcare systems since it reduces the impact of adversaries on EHR exchange and transmissions through wearable antennas. This research introduces Healthcare Data Privacy (HDP) through Backpropagation Learning (BL) to improve privacy maintenance in medical healthcare transmission services using wearable devices. The proposed method identifies the need for encrypting and decrypting the accumulated healthcare data based on data integrity verifications. It operates on two levels for verifying the security measures to prevent data losses in successive transmissions of wearable devices. Graphical abstract: Healthcare Data Privacy (HDP) using the Backpropagation Learning (BL) algorithm is utilized in the proposed method to promote privacy protection in medical healthcare transmission facilities. As a result of data integrity checks, the proposed method states that encrypted and decrypted medical records must be kept separate. Using appropriate data resources, the suggested method's performance is analysed and found superior in terms of types of experiments and accidental deletion. Image, graphical abstract