GDL

Journal from gdlhub / 2020-04-07 11:19:04
Oleh : Kashish A. Shakil, Farhana J. Zareen, Mansaf Alam, Suraiya Jabin, King Saud University
Dibuat : 2020-01-06, dengan 1 file

Keyword : Biometric, Authentication, Healthcare, Cloud, Healthcare cloud, Hadoop
Url : http://www.sciencedirect.com/science/article/pii/S1319157817301143
Sumber pengambilan dokumen : Web

Advancements in the healthcare industry have given rise to the security threat to the ever growing e-medical data. The healthcare data management system records patient’s data in different formats such as text, numeric, pictures and videos leading to data which is big and unstructured. Also, hospitals may have several branches in different geographical locations. Sometimes, for research purposes, there is a need to integrate patients’ health data stored at different locations. In view of this, a cloud-based healthcare management system can be an effective solution for efficient health care data management. But the major concern of cloud-based healthcare system is the security aspect. It includes theft of identity, tax fraudulence, bank fraud, insurance frauds, medical frauds and defamation of high profile patients. Hence, a secure data access and retrieval is needed in order to provide security of critical medical records in healthcare management system. Biometric based authentication mechanism is suitable in this scenario since it overcomes the limitations of token theft and forgetting passwords in the conventional token id-password mechanism used for providing security. It also has high accuracy rate for secure data access and retrieval. In the present paper, a cloud-based system for management of healthcare data BAMHealthCloud is proposed, which ensures the security of e-medical data access through a behavioral biometric signature-based authentication. Training of the signature samples for authentication purpose has been performed in parallel on Hadoop MapReduce framework using Resilient Backpropagation neural network. From rigorous experiments, it can be concluded that it achieves a speedup of 9 times, Equal error rate (EER) of 0.12, the sensitivity of 0.98 and specificity of 0.95. Performance comparison of the system with other state-of-art-algorithms shows that the proposed system preforms better than the existing systems in literature.

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