Privacy Preserving Encryption with Optimal Key Generation Technique on Deduplication for Cloud Computing Environment

Cloud computing performs a significant part in sharing resources and data with other devices via data outsourcing. The data collaboration services, as a potential service given by the cloud service provider (CSP), is to assist the consistency and availability of the shared data amongst users. At the...

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Vydané v:2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) s. 464 - 470
Hlavní autori: Polepaka, Sanjeeva, Gayathri, B, Ayoub, Shahnawaz, Sharma, Himanshu, Moudgil, Yudhveer Singh, Kannan, S
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 13.12.2022
Predmet:
Cloud computing
Computational modeling
Data security
Encryption
Key generation
Performance evaluation
Privacy
Process control
Renewable energy sources
Secrecy
Writing
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Shrnutí:Cloud computing performs a significant part in sharing resources and data with other devices via data outsourcing. The data collaboration services, as a potential service given by the cloud service provider (CSP), is to assist the consistency and availability of the shared data amongst users. At the time of sharing resources, it is a complicated process for providing secure writing and access control operations. This study develops a Privacy Preserving Encryption with Optimal Key Generation Technique (PPE-OKGT) for CC environment. The presented PPE-OKGT technique secures the data prior to storing in the cloud sever via encryption process. For accomplishing this, the presented PPE-OKGT technique employs data encryption technology to secure the input data into a hidden format. Besides, in order to improve secrecy, the presented PPE-OKGT technique designs a chaotic search and rescue optimization (CSRO) algorithm for optimal generation of keys. The promising performance of the PPE-OKGT technique can be verified using a set of experimentations. A comprehensive comparison study reported the enhancements of the PPE-OKGT technique over other models.
DOI:10.1109/ICACRS55517.2022.10029045