Big Data Security and Privacy: Issues, Solutions, and Research

This report provides a comprehensive overview of big data security and privacy, covering various challenges, technologies, and future research directions. It begins with an abstract outlining the core focus of the report, followed by an introduction that emphasizes the importance of big data in modern information systems. The report delves into the technologies used in big data, including distributed storage, data virtualization, data integration, and data pre-processing, and discusses the challenges faced in big data, such as privacy and security issues, data access and sharing problems, and analytic challenges. It also identifies gaps in the existing literature, highlighting areas that have not been adequately addressed and suggests future research directions, particularly in the areas of data breach and security. The report concludes by emphasizing the importance of adopting security programs to mitigate the risks associated with big data and protect user data.

Running Head: BIG DATA
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Big data security and privacy issues and solutions

Abstract
Big data is one of the important technologies in the field of information system
and the use of this technology is growing very fast. In this modern technology, many of
human beings are connected with other people with the help of various kinds of
communication system like social media, and mobile phones (Al, Neyadi, Mohamed, &
Al, 2015). People share their personal information from one destination to another by
using wireless networks and big data is processed by which people can analyze their
data or information. The main purpose of this report is to understand the fundamental
concept of big data and their security threats or challenges.
Keywords: Big data, security, threat and challenges, social media and communication
system.

Contents
Abstract.................................................................................................................................................1
Introduction...........................................................................................................................................3
Overweight of Big data..........................................................................................................................3
Technologies used in big data...............................................................................................................3
Distributed Storage............................................................................................................................3
Data Virtualization.............................................................................................................................4
Data Integration................................................................................................................................4
Data Pre-processing...........................................................................................................................4
Data Quality.......................................................................................................................................4
NoSQL process...................................................................................................................................5
Challenges faced in big data..................................................................................................................5
Privacy and security...........................................................................................................................5
Data access and sharing problem......................................................................................................5
Analytic challenges............................................................................................................................5
Scale and complexity.........................................................................................................................6
Gaps in the literature.............................................................................................................................6
Future research.....................................................................................................................................6
Conclusion.............................................................................................................................................7
References.............................................................................................................................................8

Introduction
Big data is a very important technology for any organization because many new
web-services developed by which companies cannot handle their data. The main
advantage of this innovation is that it can store large of amount of data at a time and it is
more secure rather than other storage elements (Alrawais, Alhothaily, Hu, & Cheng,
2017). The main objective of this report is to describe various kinds of threat and issues
faced in the big data and different types of methods to reduce security issues of this
technology. There are main three concepts associated with big data, for example,
volume, velocity, and variety. It uses two methods for analysis such as predictive
analytics, and user behavior analytic method which are described in this report
(Hashem, et al., 2015).
Overweight of Big data
Big data is defined as a process which is used to collect large and complex data
sets. Database system by using big data technology is an advanced innovation in the
field of the communication system (Inukollu, Arsi, & Ravuri, 2014). Now, this
information system is enhancing by the web services and data management systems
and it also implemented on many web servers. Due to increasing of web-based services,
it is very difficult to handle a large amount of data for any organization and it is
observed that this technology reduced the problem of data management (Li, Gai, Qiu,
Qiu, & Zhao, 2017). According to MGI, the big data is a type of datasets whose size is
very large as compared to database software and storage elements. Google is one of the
best examples of this technology and in which it collects data from its own services and
operation and it uses the various process to store data like voice recognition, location-
based services, and translation (Matturdi, Xianwei, Shuai, & Fuhong, 2014).
Technologies used in big data
There are many technologies used in big data analysis which are described
below

Distributed Storage
It is a way to collect independent nodes and loss of big data and it is estimated
that it can store replicated data source (Perera, et al., 2015). This is also called as non-
relational database system because it does not depend upon nodes or counters. Big data
system uses this type of technology to distribute files into various kinds of storage
elements.
Data Virtualization
It is a very common technology which is used in big data and the main purpose of
this system is that it produces retrieve data without implementing the technical
restriction. It uses Apache Hadoop and various real-time data elements to store data of
any organization (Perera, et al., 2015).
Data Integration
Data integration is a process which is used for integration purpose and many
organization uses this type of technology because it reduced time and cost. A key
operational test for most associations taking care of enormous information is to process
terabytes (or petabytes) of information in a way that can be valuable for client
expectations. Information joining devices enable organizations to streamline
information over various enormous information arrangements, for example, Amazon
EMR, Apache Hive, Apache Pig, Apache Spark, Hadoop, MapReduce, MongoDB and
Couchbase (Weber, 2015).
Data Pre-processing
It refers to a pre-evaluation process that uses programmes to store data or
information. The main objective of this technology is to manipulate the database into a
given format and which can be used for further analysis. It uses various kinds of tools
that accelerate the information sharing system by using unstructured data sets (Yang,
Li, & Niu, 2015). The main limitation of this process is that it is not an automatic system
and need user oversight that consumes more times.
Data Quality
An imperative parameter for big data preparing is the information quality. The
information quality programming can lead purging and enhancement of vast
informational indexes by using parallel preparing (Yang, Li, & Niu, 2015). These virtual

products are generally utilized for getting steady and dependable yields from enormous
information handling.
NoSQL process
While the customary SQL can be successfully used to deal with an extensive
measure of organized information, we require NoSQL (Not Only SQL) to deal with
unstructured information. NoSQL databases store unstructured information with no
specific composition. Each line can have its own particular arrangement of segment
esteems. NoSQL gives better execution in putting away an enormous measure of
information. There are many open-source NoSQL DBs accessible to investigate huge
Data (Wu, Zhu, Wu, & Ding, 2014).
Challenges faced in big data
Privacy and security
It is a most important problem faced in big data analysis and it is observed that
this technology increased various types of security risks. When private information of
any consumer combined with other databases then they can lead risks and interface
into human servers (Xu, Jiang, Wang, Yuan, & Ren, 2014). In this analysis system, users
can lose their personal information’s because it uses many software’s and networks to
store data and hackers can easily block their peripheral devices.
Data access and sharing problem
In this modern technology, many users use various security systems to secure
data from other person but attacks use flooding process by which they can control their
communication systems (Yang, Li, & Niu, 2015). By which management of data and
sharing of information are very difficult and data access is also a common problem for
big data.
Analytic challenges
Analysis of any data or information by sung big data process is very difficult
because it is less accurate rather than other technologies. The analysis process is done
on the large amount of database which may be unstructured, or structured and big data
uses the less efficient software’s through which people do not receive proper analysis
(Yang, Wu, Yin, Li, & Zhao, 2017).

Scale and complexity
It is researched that when the rate of data enhances then the volume of the
database also increases by which the problem of complexity occurs in the system.
Traditional software’s and tools are not enough to monitor and control the volumes of
data sets (Yang, Li, & Niu, 2015). It is observed that retrieval and modeling are also
common problems due to scalability and complexity of information which requires to
be analyzed.
Gaps in the literature
It is estimated that there are many authors that are researched on issues of big
data and they observed that the main problem with big data process is lack of privacy
(Hashem, et al., 2015). Big data is the latest analysis technology which can store a large
amount of data and many organizations can analyze their data sets. There are many
security threats and challenges into this technology, for example, data collection
problem, security and privacy issue, and analytics issue and complexity problem
(Perera, et al., 2015). Many writers have been addressed the issue of analysis and
complexity but the problem of data breach and security which are not addressed
(Inukollu, Arsi, & Ravuri, 2014). To improve the security of user’s personal data files
various types of technologies are developed like encryption method, cryptography, and
virtualization process. According to my opinion, the lack of security is the very biggest
problem for any modern technology and I identified that many users use low password
system by which they can lose their privacy. Hackers use botnet systems and they can
encrypt consumers personal accounts by transferring a large number of traffic signals.
We can reduce this type of challenges by adopting biometric systems and firewall
software for security purpose (Yang, Li, & Niu, 2015).
Future research
Information is a very important key element for any organization and many
users communicate with other by using communication networks and social media
through which data rate also increased (Hashem, et al., 2015). Big data decreased the
problem of data storage because this process provides a database system to store the
user's personal information. In future information and communication technology will
reduce potential threats and issues of big data and organizations can make their

strategies to avoid the problem of a data breach (Hashem, et al., 2015). Therefore the
information of any users can be secure by using encryption technique and in the future,
the organization can manage their security plans.
Conclusion
The big data technology is a combination of processing steps and various
techniques which is used to analyze the data of any community. This technology
increases with the help of traditional security solution, public clouds, and DMZ. There
are many technologies used in big data like data virtualization, data integration,
distributed storage, and pre-processing system and all these are explained in this
report. It is investigated that many organizations cannot manage their data files for
which big data analytics can be used to manage all personal data files. This report
described various kinds of challenges and issues of big data and gap in the literature.
Users should adopt security programmes like cryptography, password-based system
and firewall software to reduce problems and issues.

References
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Yang, Y., Wu, L., Yin, G., Li, L., & Zhao, H. (2017). A survey on security and privacy issues
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