scholarly journals Big data security challenges and strategies

2019 ◽  
Vol 4 (3) ◽  
pp. 860-879 ◽  
Author(s):  
Sitalakshmi Venkatraman ◽  
◽  
Ramanathan Venkatraman ◽  
Author(s):  
Fatima-Zahra Benjelloun ◽  
Ayoub Ait Lahcen

The value of Big Data is now being recognized by many industries and governments. The efficient mining of Big Data enables to improve the competitive advantage of companies and to add value for many social and economic sectors. In fact, important projects with huge investments were launched by several governments to extract the maximum benefit from Big Data. The private sector has also deployed important efforts to maximize profits and optimize resources. However, Big Data sharing brings new information security and privacy issues. Traditional technologies and methods are no longer appropriate and lack of performance when applied in Big Data context. This chapter presents Big Data security challenges and a state of the art in methods, mechanisms and solutions used to protect data-intensive information systems.


Author(s):  
Jaya Singh ◽  
Ashish Maruti Gimekar ◽  
S. Venkatesan

Big Data is a very huge volume of data which is beyond the storage capacity and processing capability of traditional system. The volume of data is increasing at exponential rate. Therefore, there is the need of such mechanism to store and process such high volume of data. The impressiveness of the Big data lies with its major applicability to almost all industries. Therefore, it represents both, the tremendous opportunities and complex challenges. Such omnipotent eminence leads to the privacy and security related challenges to the big data. Nowadays, security of big data is mainly focused by every organization because it contains a lot of sensitive data and useful information for taking decisions. The hostile nature of digital data itself has certain inherited security challenges. The aim of Big data security is to identify security issues and to find the better solution for handling security challenges. The observation and analysis of different security mechanism related to the issues of big data and their solutions are focused in this chapter.


Web Services ◽  
2019 ◽  
pp. 25-38 ◽  
Author(s):  
Fatima-Zahra Benjelloun ◽  
Ayoub Ait Lahcen

The value of Big Data is now being recognized by many industries and governments. The efficient mining of Big Data enables to improve the competitive advantage of companies and to add value for many social and economic sectors. In fact, important projects with huge investments were launched by several governments to extract the maximum benefit from Big Data. The private sector has also deployed important efforts to maximize profits and optimize resources. However, Big Data sharing brings new information security and privacy issues. Traditional technologies and methods are no longer appropriate and lack of performance when applied in Big Data context. This chapter presents Big Data security challenges and a state of the art in methods, mechanisms and solutions used to protect data-intensive information systems.


Web Services ◽  
2019 ◽  
pp. 13-24
Author(s):  
Jaya Singh ◽  
Ashish Maruti Gimekar ◽  
S. Venkatesan

Big Data is a very huge volume of data which is beyond the storage capacity and processing capability of traditional system. The volume of data is increasing at exponential rate. Therefore, there is the need of such mechanism to store and process such high volume of data. The impressiveness of the Big data lies with its major applicability to almost all industries. Therefore, it represents both, the tremendous opportunities and complex challenges. Such omnipotent eminence leads to the privacy and security related challenges to the big data. Nowadays, security of big data is mainly focused by every organization because it contains a lot of sensitive data and useful information for taking decisions. The hostile nature of digital data itself has certain inherited security challenges. The aim of Big data security is to identify security issues and to find the better solution for handling security challenges. The observation and analysis of different security mechanism related to the issues of big data and their solutions are focused in this chapter.


2018 ◽  
Vol 6 (7) ◽  
pp. 981-985
Author(s):  
Dinesh Singh ◽  
Dayanand . ◽  
Arushi Arya
Keyword(s):  
Big Data ◽  

2020 ◽  
Vol 13 (4) ◽  
pp. 790-797
Author(s):  
Gurjit Singh Bhathal ◽  
Amardeep Singh Dhiman

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.


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