A Novel Inconsequential Encryption Algorithm for Big Data in Cloud Computing

• Autorzy: Motupalli Ravi Kanth , Prasad Krishna K.

Identyfikatory

DOI

10.35784/jcsi.2913

• Języki publikacji: EN

Abstrakty

EN

In the digitalized era of the information technology the expansion of the data usage is very high accounting for about enormous data transaction in day to day life. Data from different sources like sensors, mobile phones, satellite, social media and networks, logical transaction and ventures, etc add an gigantic pile to the existing stack of data. One of the best way to handle this exponential data production is the Hadoop network. Thus in the current scenario big industries and organizations rely on the Hadoop network for the production of their essential data. Focusing on the data generation and organization, data security one of the most primary important consideration was left unnoticed making data vulnerable to cyber attacks and hacking. Hence this article proposes an effective mixed algorithm concept with the Salsa20 and AES algorithm to enhance the security of the transaction against unauthorised access and validates the quick data transaction with minimal encryption and decryption time. High throughput obtained in this hybrid framework demonstrates the effectiveness of the proposed algorithmic structure over the existing systems

Słowa kluczowe

EN

Hadoop network; data security; cyber attacks; Salsa20

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Journal of Computer Sciences Institute
• Rocznik: 2022
• Tom: Vol. 23
• Strony: 140--144
• Opis fizyczny: Bibliogr. 28 poz., fig.

Twórcy

autor

Motupalli Ravi Kanth

• nstitute of Computer Science & Information Sciences, Srinivas University, Mangalore, Karnataka, India
• Department of CSE, VNR VJIET, Hyderabad, Telangana, India

autor

Prasad Krishna K.

• Institute of Computer Science & Information Sciences, Srinivas University, Mangalore, Karnataka, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).