Efficient secure data retrieval on cloud using multi-stage authentication and optimized blowfish algorithm

Cloud computing is currently playing an important role in the information technology industry because of its improved efficiency, wide access, low cost, and many benefits. It also provides more space for storing data and transmitting data from one location to another faster for different users on th...

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Vydané v:Journal of ambient intelligence and humanized computing Ročník 13; číslo 1; s. 151 - 163
Hlavní autori: Shyla, S. Immaculate, Sujatha, S. S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2022
Springer Nature B.V
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ISSN:1868-5137, 1868-5145
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Shrnutí:Cloud computing is currently playing an important role in the information technology industry because of its improved efficiency, wide access, low cost, and many benefits. It also provides more space for storing data and transmitting data from one location to another faster for different users on the Internet. Due to large storage, cloud customers can save huge capital investment on IT infrastructure and focus on their own core business. Therefore, many companies or organizations are moving their business to the cloud. However, many customers are reluctant to use the cloud due to security and privacy concerns. To tackle this problem, in this paper, efficient secure data retrieval is developed with the help of multi-stage authentication (MSA) and optimized blowfish algorithm (OBA). The proposed system consists of three modules namely, MSA, data security, and data retrieval. Initially, the cloud users register their information on cloud based on a multi-authentication procedure. After the registration process, the data are encrypted with the help of OBA. To increase the security of the system, the key value is optimally selected with the help of a binary crow search algorithm. After the encryption process, MSA based data retrieval process is performed. This will avoid, un-authorized person to attack the data. The performance of the proposed methodology is implemented in JAVA and performances are analyzed in terms of different metrics.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-021-02893-8