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- ISSN: 1872-2121
- E-ISSN: 2212-4047
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CAML: Cryptographic-Based Cloud Security for Healthcare Data with Machine Learning Technique
- Source: Recent Patents on Engineering, Volume 19, Issue 2, Jan 2025, E241023222580
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- 27 Feb 2023
- 24 Jul 2023
- 01 Feb 2025
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 patent paper aims to propose a security model for attack prevention within the healthcare environment.
Methods
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.
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