A review on Big Data and its security

Tiwari, Abhishek; Chaudhary, Hemlata; Yadav, Surendra Kumar

doi:10.1109/iciiecs.2015.7193110

Cited by 12 publications

(7 citation statements)

References 5 publications

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“…The complexity of controlling and maintaining big data in diverse fields of science is described in [2]. The Map Reduce High Performance Computer Cluster HPCC, the Knowledge Data Discovery, and data mining methods are used to define data sets and extract the required information directly.…”

Section: Related Workmentioning

confidence: 99%

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An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data Mobility

et al. 2019

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The expand trend of cloud data mobility led to malicious data threats that necessitate using data protection techniques. Most cloud system applications contain valuable and confidential data, such as personal, trade, or health information. Threats on such data may put the cloud systems that hold these data at high risk. However, traditional security solutions are not capable of handling the security of big data mobility. The current security mechanisms are insufficient for big data due to their shortage of determining the data that should be protected or due to their intractable time complexity. Therefore, the demand for securing mobile big data has been increasing rapidly to avoid any potential risks. This paper proposes an integrated methodology to classify and secure big data before executing data mobility, duplication, and analysis. The necessity of securing big data mobility is determined by classifying the data according to the risk impact level of their contents into two categories; confidential and public. Based on the classification category, the impact of data security is studied and substantiated on the confidential data in the scope of Hadoop Distributed File System. It is revealed that the proposed approach can significantly improve the cloud systems data mobility.INDEX TERMS Big data classification, data security, metadata, risk impact level, HDFS.

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Section: Related Workmentioning

confidence: 99%

“…The escalated use of information resources and the need of advanced data processing technologies lead to the existence of big data. An overview of big data variety, volume, security and privacy are discussed in [2].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data Mobility

et al. 2019

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“…Big data platforms offer HDFS tools for computing big data by making decisions quickly and minimizing the risk of human intruders [4]. HDFS, which is commonly used in big data and cloud processing, involves splitting data into small files; this system supports parallel computation and has high reliability, reduced redundancy, and improved scalability in distributed systems [5]. In addition, HDFS is designed to process large datasets and data types (i.e., structured, unstructured, and semistructured).…”

Section: Introductionmentioning

confidence: 99%

Protecting Data Mobility in Cloud Networks Using Metadata Security

Punithavathi¹,

Kowsigan²,

Shanthakumari³

et al. 2022

Computer Systems Science and Engineering

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At present, health care applications, government services, and banking applications use big data with cloud storage to process and implement data. Data mobility in cloud environments uses protection protocols and algorithms to secure sensitive user data. Sometimes, data may have highly sensitive information, leading users to consider using big data and cloud processing regardless of whether they are secured are not. Threats to sensitive data in cloud systems produce high risks, and existing security methods do not provide enough security to sensitive user data in cloud and big data environments. At present, several security solutions support cloud systems. Some of them include Hadoop Distributed File System (HDFS) baseline Kerberos security, socket layer-based HDFS security, and hybrid security systems, which have time complexity in providing security interactions. Thus, mobile data security algorithms are necessary in cloud environments to avoid time risks in providing security. In our study, we propose a data mobility and security (DMoS) algorithm to provide security of data mobility in cloud environments. By analyzing metadata, data are classified as secured and open data based on their importance. Secured data are sensitive user data, whereas open data are open to the public. On the basis of data classification, secured data are applied to the DMoS algorithm to achieve high security in HDFS. The proposed approach is compared with the time complexity of three existing algorithms, and results are evaluated.

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“…About 500 GIGABYTE (GB) of data can store by a single digital computer, hence about twenty (20) billion Computers would need to hold the entire world's data. Human genome decryption procedure gets just about ten (10) years in the past, while now a day's the same process takes a week or two [16]. On the web backbone traffic, audio, video and graphical data have huge mass and is estimated to raise seventy percent (70%) by 2013 [3].…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Recent Advances, Characteristics and Challenges of Big Data

Khattak¹,

Khan²,

Khan³

et al. 2018

ICST Transactions on Scalable Information Systems

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Here in this study, we provide an empirical analysis of recent advances, characteristic and challenges of big data. Initially, we acquaint the readers with the general background, history, and characteristics of big data including volume, velocity, value and variety etc. The scope of applications for big data including political services and government monitoring, enterprise management, scientific research, public utilities, public administration and internet of things are illustrated. A detailed analysis is presented regarding opportunities and challenges faced by the public and private sectors during analysis phase of big data management such as storing, visualizing, capturing and so on. In addition, we investigated and reported a detailed empirical analysis of the most recent management tools like Hadoop and MapReduce, along with their different components, usage, and limitation. Finally, open issues and future directions for this new and dynamic area of research are provided. The primary objective of this empirical analysis is to present a broad-spectrum perspective of this emerging research area with the goal to present big data related concepts in a coherent manner to the beginners.

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A review on Big Data and its security

Cited by 12 publications

References 5 publications

An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data Mobility

An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data Mobility

Protecting Data Mobility in Cloud Networks Using Metadata Security

Empirical Analysis of Recent Advances, Characteristics and Challenges of Big Data

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