Hybrid Security Model for Medical Image Protection in Cloud

https://doi.org/10.24237/djes.2023.16107

Authors

  • Mohammed Y. Shakor Research Centre, University of Garmian, Kalar, Sulaimani, Kurdistan Region, Iraq
  • Nigar M. Shafiq Surameery Information Technology Department, College of Computer and Information Technology, University of Garmian, Sulaimani, Kurdistan Region, Iraq
  • Zuheir N. Khlaif Faculty of Educational Sciences, An Najah National University, Nablus, Palestine

Keywords:

Cloud Storage, Cloud Security, AES, RSA, Healthcare Data

Abstract

A cloud computing environment provides a cost-effective approach for end-users to remotely store and retrieve private data through an internet connection anytime and anywhere. The security of these data cannot always be guaranteed because they can only be accessed by the end-user through a third-party interface, making them vulnerable to potential breaches of authentication and data integrity. This paper presents a secure hybrid approach for a medical image stored in the cloud that prioritises data security and integrity. The suggested model uses a combination of elliptic curve cryptography and advanced encryption standard algorithms to ensure authentication and data integrity. Results demonstrate the superior performance of the proposed model to existing methods, aligning it with compliance requirements for handling sensitive data stored in the cloud. This approach includes adherence to regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), which prescribe specific guidelines for the secure handling, storage and processing of personal information. Contrary to traditional and other hybrid systems, this study suggests that this approach is one of the best in guaranteeing the security of medical images in the cloud.

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Published

2023-03-01

How to Cite

[1]
M. Y. Shakor, N. M. S. Surameery, and Z. N. Khlaif, “Hybrid Security Model for Medical Image Protection in Cloud”, DJES, vol. 16, no. 1, pp. 68–77, Mar. 2023.