Data protection on hadoop distributed file system by using encryption algorithms: a systematic literature review

Big data has capability to process huge amount of unstructured and structured data. Nowadays, technology is able to support business need by extracting massive amount of data and recognizing its pattern to predict future trends. It brings right insight in business strategy to gain tremendous benefit...

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Vydané v:Journal of physics. Conference series Ročník 1444; číslo 1; s. 12012 - 12019
Hlavní autori: Naisuty, Meisuchi, Nizar Hidayanto, Achmad, Clydea Harahap, Nabila, Rosyiq, Ahmad, Suhanto, Agus, Michael Samuel Hartono, George
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bristol IOP Publishing 01.01.2020
Predmet:
Algorithms
Big Data
Data encryption
Literature reviews
Pattern recognition
Security
Structured data
Unstructured data
ISSN:1742-6588, 1742-6596
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Popis
Shrnutí:Big data has capability to process huge amount of unstructured and structured data. Nowadays, technology is able to support business need by extracting massive amount of data and recognizing its pattern to predict future trends. It brings right insight in business strategy to gain tremendous benefit. Hadoop is a reliable technology which developed to distribute process and storage on big data efficiently. However, Hadoop doesn't have any built-in provision to encrypt data by default. Hadoop additional feature in encryption zone has security issue which key management does outside of HDFS. Sensitive and confidential data in HDFS can be exposed against security attack. Information security is fundamental concern and new set challenge for the world of big data. The main purpose of this paper is to protect Hadoop Distributed File System data by using encryption algorithm. This is to ensure data is secured at storage level of HDFS. Dealing with big data, it is important to choose fast enough encryption algorithm that has great performance. Research methodology is SLR (System Literature Review) by using methodology of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses).
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1444/1/012012