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Research Article

A secure authentication and collaborative data-sharing model based on a blockchain network in the cloud

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Received 06 Feb 2023, Accepted 06 Dec 2023, Published online: 28 Apr 2024
 

ABSTRACT

In cloud computing, various organisations store their confidential information in private or public cloud platforms. The data sharing through the various cloud platforms increases the risk of security. To attain data sharing while sustaining data confidentiality, a secure sharing platform and collaboration model is needed. Hence, this paper introduces the novel secure authentication and collaborative data sharing (SecAuth_CDS) scheme for sharing data on blockchain. The entities involved in the SecAuth_CDS are third parties, data owners, smart contracts and blockchain networks. The security of the authentication process can be boosted by applying various security operations, such as Exclusive OR (EXOR), hashing, encryption and so on. The proposed method assures that the sharing is secure and trustworthy by blockchain. The experimental result reveals that the SecAuth_CDS performed effectively in terms of revenue, memory usage and computation cost with the values of 27, 62.20 MB and 0.003 sec.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

B. Samuel

Mr. B. Samuel is a research scholar at Vels University. He has obtained his undergraduate and postgraduate degrees from University of Madras and M.Phil from Vinayaga Mission University. Currently he is working as an Assistant Professor in MCA Department at Gurunanak college.

K. Ksaturi

Dr. K. Ksaturi has been working as an associate professor at Vels Institute of Science Technology and Advanced Studies (VISTAS) in the department of Information Technology, School of Computing Sciences. She has more than 16 years of teaching experience and more than 10 years of research experience. She has published more than 30 articles in a reputed journals. She has received Young Researcher award and Best Faculty award. Her research interest includes big data, machine learning, transfer learning and deep learning.

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