CAML: Cryptographic-Based Cloud Security for Healthcare Data with Machine Learning Technique

Title:CAML: Cryptographic-Based Cloud Security for Healthcare Data with Machine Learning Technique

Volume: 18

Author(s): Chaithra M H*Vagdevi S

Affiliation:

• Department of Computer Science and Engineering, REVA University & Research Scholar, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India

Abstract: Background: The Cloud model is one of the most realistic frameworks with a vast range of social networking interactions. In medical data, security is a major constraint as it incorporates information about the patients. The cloud environment subjected to mobility and openness is exposed to security issues and limits authorization levels for data transmission.

Objective: This paper aims to propose a security model for attack prevention within the healthcare environment.

Method: The proposed Cryptographic Attribute-based Machine Learning (CAML) scheme incorporates three stages. Initially, the homomorphic encryption escrow is performed for secure data transmission in the cloud. Secondly, the information of the users is evaluated based on the consideration of users' authorization. The authorization process for the users is carried out with the attribute-based ECC technique. Finally, the ML model with the classifier is applied for the detection and classification of attacks in the medical network.

Results: The detected attack is computed and processed with the CNN model. Simulation analysis is performed for the proposed CAML with conventional ANN, CNN, and RNN models. The simulation analysis of proposed CAML achieves a higher accuracy of 0.96 while conventional SVM, RF, and DT achieve an accuracy of 0.82, 0.89 and 0.93, respectively.

Conclusion: With the analysis, it is concluded that the proposed CAML model achieves higher classification accuracy for attack detection and prevention in the cloud computing environment.

Cite this article as:

M H Chaithra*, S Vagdevi, CAML: Cryptographic-Based Cloud Security for Healthcare Data with Machine Learning Technique, Recent Patents on Engineering 2024; 18 () : e241023222580 . https://dx.doi.org/10.2174/0118722121241098230926064800

DOI https://dx.doi.org/10.2174/0118722121241098230926064800	Print ISSN 1872-2121
Publisher Name Bentham Science Publisher	Online ISSN 2212-4047