@Article{cmc.2024.046265,
AUTHOR = {Umi Salma Basha, Shashi Kant Gupta, Wedad Alawad, SeongKi Kim, Salil Bharany},
TITLE = {Fortifying Healthcare Data Security in the Cloud: A Comprehensive Examination of the EPM-KEA Encryption Protocol},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {79},
YEAR = {2024},
NUMBER = {2},
PAGES = {3397--3416},
URL = {http://www.techscience.com/cmc/v79n2/56395},
ISSN = {1546-2226},
ABSTRACT = {A new era of data access and management has begun with the use of cloud computing in the healthcare industry. Despite the efficiency and scalability that the cloud provides, the security of private patient data is still a major concern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentiality and integrity of healthcare data in the cloud. The computational overhead of encryption technologies could lead to delays in data access and processing rates. To address these challenges, we introduced the Enhanced Parallel Multi-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the secure storage of critical patient records in the cloud. The data was gathered from two categories Authorization for Hospital Admission (AIH) and Authorization for High Complexity Operations. We use Z-score normalization for preprocessing. The primary goal of implementing encryption techniques is to secure and store massive amounts of data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become more widely available if security issues can be successfully fixed. As a result of our analysis using specific parameters including Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energy consumption (53%), the system demonstrated favorable performance when compared to the traditional method. This suggests that by addressing these security concerns, there is the potential for broader accessibility to cloud storage solutions for safeguarding healthcare data.},
DOI = {10.32604/cmc.2024.046265}
}