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Erschienen in: Neural Computing and Applications 2/2020

01.02.2019 | Review Article

An enhanced secure content de-duplication identification and prevention (ESCDIP) algorithm in cloud environment

verfasst von: J. K. Periasamy, B. Latha

Erschienen in: Neural Computing and Applications | Ausgabe 2/2020

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Abstract

In cloud computing, de-duplication plays an essential role in detecting the de-duplication of encoded data with minimal computation and cost. De-duplication cleans the cloud datacentre’s unwanted storage and helps to identify the right owner of the content in the cloud. Even if there is only one copy of each data file stored in the cloud, the cloud has huge quantity of cloud users who own such data file. The existing method discussed a convergent encryption technique to solve the de-duplication problem. It also developed a system which does not allow storing any duplicate data in the cloud. However, the method does not assure consistency, reliability and confidentiality in cloud. Similar or different cloud users could store duplicated file in the cloud server, where cloud storage utilises high volume of storage. To find a solution for the above problems, the paper introduces enhanced secure content de-duplication identification and prevention (ESCDIP) algorithm to enhance the file-level and content-level de-duplication detection of encoded data with reliability in cloud environment. Every cloud user’s files contain an independent master key for encryption using ESCDIP technique and outsourcing them into the cloud. It reduces the overheads that are associated with the interactive duplication detection and query processes. The proposed method identifies the unique data chunking to store in the cloud. Based on experimental result, the ESCDIP method reduces 2.3 data uploading time in seconds, 2.31 data downloading time in seconds and 32.66% communication cost compared to existing approaches.

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Metadaten
Titel
An enhanced secure content de-duplication identification and prevention (ESCDIP) algorithm in cloud environment
verfasst von
J. K. Periasamy
B. Latha
Publikationsdatum
01.02.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04060-9

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