Secure Medical Data Model Using Integrated Transformed Paillier and KLEIN Algorithm Encryption Technique with...

Area of Science:

• Computer Science
• Biomedical Engineering
• Information Security

Background:

• Healthcare data security is paramount due to sensitive patient information and privacy regulations.
• E-health security requires robust measures for data integrity and confidentiality.
• Existing encryption methods may not be lightweight enough for real-time e-health applications.

Purpose of the Study:

• To propose a novel, lightweight encryption technique for securing electroencephalographic signals (EEGs) in healthcare.
• To enhance the confidentiality and integrity of patient data during storage and transmission.
• To optimize key space management in lightweight encryption using heuristic algorithms.

Main Methods:

• Development of the integrated transformed Paillier and KLEIN algorithms with elephant herd optimizations (ITPKLEIN-EHO).
• Utilizing elephant herd optimizations (EHOs) to adjust iteration rounds for optimizing key spaces.
• Implementation and testing of the ITPKLEIN-EHO technique using MATLAB on various EEG datasets.

Main Results:

• The ITPKLEIN-EHO technique demonstrated effective encryption of EEG signals for secure transmission.
• Performance evaluation showed competitive results compared to existing methods in terms of MSE, PSNR, SSIM, and PRD.
• The proposed method offers lightweight features suitable for e-health security applications.

Conclusions:

• The ITPKLEIN-EHO approach provides a secure and efficient solution for protecting sensitive EEG data in e-health environments.
• The integration of EHOs effectively optimizes key space, enhancing the performance of lightweight encryption.
• This method contributes to improving the overall security and privacy of electronic health records.