Abstract
The security of medical data in the cloud is the key consideration of cloud customers. While publishing the medical data, the cloud distributor may suffer from data leakages and attacks such that the data may leak. In order to resolve this, this article devises the developed Adaptive Fractional Brain Storm Integrated Whale Optimization Algorithm (AFBS_WOA), which is the hybridization of Adaptive Fractional Brain Storm Optimization (AFBSO) and Whale Optimization algorithm (WOA). The developed AFBS_WOA algorithm generates the key matrix coefficient for retrieving the perturbed database in order to preserve the privacy of healthcare data in the cloud. The developed AFBS-WOA scheme utilized the fitness function involving utility and privacy measures for calculating the secret key. Here, the privacy-preserved database is obtained by multiplying the input database with a key matrix based on developed AFBS-WOA using the Tracy–Singh product. For data retrieval, the secret key is shared with the service provider in order to retrieve the database, and then the data are accessed. Moreover, the experimental result demonstrates that the developed AFBS_WOA model attained the maximum utility and privacy measure of 0.1872 and 0.8755 using the Hungarian dataset.
Highlights
Healthcare involves various complex processes, such as treatment, diagnosis, prevention, and injury
It is clearly declared that the developed model attained the maximum privacy of 0.1872 and maximum utility of 0.8755, correspondingly. e existing methods, like Retrievable General Additive Strategy Database (RGADB), Genetic Algorithm (GA), Whale Optimization algorithm (WOA), Genetic-WOA and BS-WOA, attained the privacy of 0.1055, 0.1076, 0.1077, 0.1527, and 0.1715 and the utility of 0.7355, 0.7753, 0.7755, 0.7853, and 0.8752. e comparative analysis clearly shows that the proposed AFBS-WOA achieves the maximum privacy and utility parameters compared with the other discussed existing methods
From the observations of our proposed system, we propose that it is likely to overcome the drawback of the Genetic Grey Wolf Optimization Algorithm (GGWO) with the maximum fitness value, privacy, and utility by enhancing the function using the proposed developed AFBS-WOA
Summary
E security of medical data in the cloud is the key consideration of cloud customers. While publishing the medical data, the cloud distributor may suffer from data leakages and attacks such that the data may leak. E developed AFBS_WOA algorithm generates the key matrix coefficient for retrieving the perturbed database in order to preserve the privacy of healthcare data in the cloud. E developed AFBS-WOA scheme utilized the fitness function involving utility and privacy measures for calculating the secret key. The privacy-preserved database is obtained by multiplying the input database with a key matrix based on developed AFBS-WOA using the Tracy–Singh product. The experimental result demonstrates that the developed AFBS_WOA model attained the maximum utility and privacy measure of 0.1872 and 0.8755 using the Hungarian dataset
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