JOURNAL ARTICLE

Blockchain-Assisted Access Control with Shuffled Shepherd Fire Hawk Optimisation-Key Generation for Privacy Protection in Cloud.

  • Published In: Journal of Information & Knowledge Management, 2024, v. 23, n. 3. P. 1 1 of 3

  • Database: The Belt and Road Initiative Reference Source 2 of 3

  • Authored By: Gutte, Vitthal Sadashiv; Hande, Yogita; Deokate, Sarika Tanaji; Bhosale, Poonam Chandrakant; Kulkarni, Yogesh R. 3 of 3

Abstract

Cloud is a model of computing providing on-demand availability of computer system resources, mainly data storage and computing power, without the user's active management. This provides sharing and reduces user computing and storage costs. With the development of cloud scale and intensification, cloud security is becoming a vital issue in the cloud computing field. Here, a newly optimised hybridisation algorithm, namely, Shuffled Shepherd Fire Hawk Optimization (SSFHO) algorithm is proposed for key generation to initiate privacy protection in the cloud. The different entities involved in this approach are Data Owner (DO), Data User (DU), blockchain, and Cloud Service Provider (CSP). Here, the access control process includes seven categories, such as initialisation, registration, generation of key, data access control, authorisation, authorisation revocation and data protection. Also, registration is done in three phases, like registration of cloud, registration of user and resource publishing. Moreover, SSFHO is the integration among Shuffled Shepherd Optimization Algorithm (SSOA) and Fire Hawk Optimization (FHO) algorithm. The performance of the model is done with various performance metrics like, authorisation time, privacy rate, and memory. These revealed high values of privacy rate of 0.93, low values of authorisation time and memory of 0.559 and 0.843 GHz. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Information & Knowledge Management. 2024/06, Vol. 23, Issue 3, p1
  • Document Type:Article
  • Subject Area:Information Technology
  • Publication Date:2024
  • ISSN:0219-6492
  • DOI:10.1142/S021964922450014X
  • Accession Number:178117019
  • Copyright Statement:Copyright of Journal of Information & Knowledge Management is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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