Abstract
Recently, its becomes easy to track down the data due to its availability in a large number. Although for data management, processing, and obtainability, cloud computing is considered a well-known approach for organizational development on the internet. Despite many advantages, cloud computing has still numerous security challenges that can affect the big-data usage on cloud computing. To find the security issues/challenges that are faced by software vendors’ organizations we conducted a systematic literature review (SLR) through which we have find out 103 relevant research publications by developing a search string that is inspired by the research questions. This relevant data was comprised from different databases e.g. Google Scholar, IEEE Explore, ScienceDirect, ACM Digital Library, and SpringerLink. Furthermore, for the detailed literature review, we have accomplished all the steps in SLR, for example, development of SLR protocol, Initials and final assortment of the relevant data, data extraction, data quality assessment, and data synthesis. We identified fifteen (15) critical security challenges which are: data secrecy, geographical data location, unauthorized data access, lack of control, lack of data management, network-level issues, data integrity, data recovery, lack of trust, data sharing, data availability, asset issues, legal amenabilities, lack of quality, and lack of consistency. Furthermore, sixty four (64) standard practices are identified for these critical security challenges using the proposed SLR that could help vendor organizations to overcome the security challenges for big data. The findings of our research study demonstrate the resemblances and divergences in the identified security challenges in different periods, continents, databases, and methods. The proposed SLR will also support software vendor organizations for securing big data on the cloud computing platforms. This paper has the following content: in <xref ref-type="sec" rid="sec2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Section II</xref> , we have describe the Literature review; in <xref ref-type="sec" rid="sec3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Section III</xref> , research methodology is specified; in <xref ref-type="sec" rid="sec4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Section IV</xref> , the findings of the SLR and the analysis of result are discussed; in <xref ref-type="sec" rid="sec5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Section V</xref> , the limitations of this research are given; in <xref ref-type="sec" rid="sec6" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Section VI</xref> , we discussed our conclusions and future work.
Highlights
From a decade data is generated very speedily and can be accessed very with effective cost by leaps and bounds [1, 2]
We contributed in certain aspects, Firstly, we have find out through systematic literature review (SLR) that there are many security challenges that big data faces on the platform of cloud computing
The critical security challenges of big data usage on the platform of cloud computing are identified along with their occurrences in each research paper included in the SLR are: Data Secrecy Issue with (97%), meaning that the data secrecy issue has been discussed in 100 research papers included in the SLR, Geographical data location issue with (69%), Unauthorized data access Issue with (65%), Lack of Control with (60%), Lack of Data Management having (59%), Network level issues having (58%), Data integrity issues with (56%), Data Recovery issues with (55%), Lack of Trust with (54%), Data Sharing Issue with (53%), Data Availability with (47%), Assets Issue with (35%), Legal Amenabilities with (33%), Lack of quality issues with (25%), and Lack of consistency with (25%)
Summary
From a decade data is generated very speedily and can be accessed very with effective cost by leaps and bounds [1, 2] These data may be increased from TB to PB because 2.5 quintillion bytes of data may be gathered per day, according to Walmart, they can group and store about 2.5 PB of data just in an hour [3,4,5]. IDC’s (International Data Corporation) verified the ratio of structured data in internet is around 32% while un-structured data is around 63% [8] This data either in any form can generate big data whenever its volume, velocity, variety, Variability, value, Visualization, or veracity surpasses the volume of IT system for the storage, processing, and management of that data[4, 9, 10]. During 2000s, the International Data Corporation (IDC), an international leading market firm reports that that digital world which was 4.4 ZB in the year of 2003, will be grown up-to 44 ZB by 2020 [30, 33]
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