IT Security and Big Data: Addressing Concerns, Future Trends Report

This report delves into the critical domain of big data security, examining the multifaceted challenges and opportunities that arise in the context of cybersecurity. It begins with an introduction that highlights the growing importance of data security in various enterprises, followed by a background section that outlines the evolution of big data and its implications for security. The core of the report focuses on the security and privacy concerns associated with big data, including privacy risks, credibility issues, cryptographic protection challenges, and the absence of security audits. The report then proposes several strategies for addressing these concerns, such as improving privacy protection mechanisms, adopting advanced authentication techniques, implementing secure communication protocols, enhancing internal security measures, and utilizing encryption techniques and perimeter-based security. The report concludes by emphasizing the importance of robust security protocols and consumer privacy, and it also discusses future trends, such as the integration of big data with emerging technologies like IoT, data mining, and quantum computing. The report underscores the need for organizations to proactively address security risks and adapt to the evolving landscape of big data and cybersecurity.

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Table of Contents
Introduction................................................................................................................................2
Background................................................................................................................................2
Security and privacy concerns...................................................................................................3
Strategies for addressing the concerns.......................................................................................5
Conclusion and future trends.....................................................................................................7
References..................................................................................................................................8

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Introduction
Data security has been of great concern in many enterprises. In addition, there are
various dynamics in the data storage systems and technology impact the need for data
security. In the market, there is number of cutting edge technologies that executives and
managers need to be fear like increased use of internet, cloud servers and storage and various
other networking systems (Kshetri, 2014).
Big data is categorized by its variety, volume, veracity and value. To identify the
validity and value of data, it must first be processed and analyzed by organizations who look
to gain benefit from such information. In today’s digital age, big data analysis shows a
significant role in enterprises by contributing to their marketing strategies by building digital
portfolios on consumers through data and information attained from social media sites,
purchase history and search engines (Xu et al, 2014). These data are analyzed by the
company to explore customer preferences, determine trends and thus enable organizations to
market their services or products as per the expectations and interest of consumer. Though,
there are some drawbacks too in related to these big data projects considering privacy and
ethical concerns.
This report highlight all these concerns and other security and privacy factors linked
with big data. In addition, several strategies are also recommended for addressing these
concerns and in last, a conclusion is derived considering the future trends.
Background
Today’s information-age has led to cyberspace becoming a progressively important
feature. There is a continuous rise in security breach in information with relation to big data
security. Big data security is also a concern of cyber security and as per the report released by
computer security institute; it was found that almost 66% of all the data security breach
incidents in enterprises tend to be conducted from within the organisation by authorized user
(Tene & Polonetsky, 2012). In this emerging technology, big data basically identify
repositories which are beyond the capability of current applications of the database
management system considering management, analyzation and storage facilities for these

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repositories. Rubinstein (2012) also stated that amount of information managed by
organizations datacenters will rise by 50 times and data volume continues to increase as they
took in an extended range of sources. This will ruin the traditional defensive environments
and so enterprises will be needed to adopt an intelligence-driven security model depend on on
big data analytics. For instance, attackers may sabotage the organizations, if they kept data at
one place. Hence, it requires that big data information be rightly controlled with having
principles such as abridged rights, mainly for access rights, excluding from an overseer who
has authority to physically access the data (Chan & Bennett Moses, 2017).
However, the reality is that security cannot only be efficiently attained only with
anonymous protection. For instance, some of the records in the search history of an
organisation can be used by the management in an anonymous way within 3 months for use
by the individual. Though, the identification information confined therein and has been
prudently managed, many records content confined therein can be effectively well-defined.
Moreover, users and organizations also are less aware with lack of self-protection and this
leads to many losses in extent with leakage of information (Singh et al, 2015).
Security and privacy concerns
The adoption of Big Data bases brings out various challenges and concerns and leads
high expectation of gleaning new value intuition from existing data sources. Moreover, in
today’s time, every enterprise is awash in a flood of data flow where the data is collected at
eccentric rate. The initial shift of adopting big data in the company leverage various
enterprises to use big data technology applications, however, after several different phases,
several issues started coming out possess cyber security threat to all of enterprises (Esposito
et al, 2018).
In Big data security challenges, the foremost is the privacy risks as while users taking
advantage of expediency brought by big data, they also meet various drawbacks too. While in
the process of use, if big data did not protect well the user data, the privacy of users will be
hampered and it will not be considered as traditional issues of personal privacy however also
founded on the research and analysis of individuals’ information. In existing scenario, many
organizations believe that the process of information is done anonymously with hidden

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identifiers and then the data will be released. However, in actuality, with only anonymous
protection, one cannot effectively achieve protection of privacy (Andrejevic, 2014).
The other security concern is that big data credibility is not getting confirmed and the
basis of big data analysis is these information will inevitably cause to erroneous outcomes
and some of users are developing data illusions that are valuable to them causing individuals
to make incorrect decisions (Allen, 2016). For instance, on various sites, there are some false
feedbacks and comments which induce users or customer to buy inferior goods after seeing
those fake comments. Considering current web popularity, it is not possible to measure the
impact of this false information and screening of these data is also very challenging. Other
than this, due to the potential presence of untrusted mappers, various cybercriminals can
make mappers produce an insufficient list of value/key pairs and also this enables outsiders to
gain sensible information. The problem is that one can easily access to these things as big
data technologies are not providing any sort of additional security layer concerning with
protection of data. This makes relying on perimeter security systems which are of no use
while protecting big data.
Other challenge is troubles of cryptographic protection, as these security measures get
ignored with the possibility of big data. Sensitive data is just embedded into various cloud
platforms without any encrypted measures and security protection (Tien, 2013). The reason is
simple as continuous encryption and decryption of these massive data slow down the things
which is one of the initial advantages of many companies while providing various services to
the users i.e. speed.
For the protection of big data, many enterprises adopt perimeter-based security which
offers services only to entry and exit gate to a platform. However, inside the box will always
be a mystery as corrupt IT specialist or any rival may mine the data and use it for their own
benefit causing huge loss to the company in terms of cost and good will (Yang et al, 2015).
Other than this, one of the most significant securities and privacy concerns linked with big
data is absence of security audits that help organizations to gain awareness of their security
gaps.
Hence, working with big data has enough concerns and challenges requires much
efforts, time and resources. However, with carefully design big data adoption plan, the
company can create various solutions they required. Below, there are listed various strategies
to address such concerns (Spiekermann et al, 2015).

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Strategies for addressing the concerns
Issue faced in big data security can be resolved via merging of big data with recent
technologies in the technology industry as well as required practices adopted by big firms
while dealing out with such challenge.
The first strategy that an organisation can adopt by improving the privacy protection
legal mechanism, as with the development of people and society, the organisation need to
deploy certain measures for the protection of their data. This can be done by creating a
privacy protection agency or department that can effectively crack down infringements of
measures in relation to organisation practices and thus build a safer environment (Lu et al,
2014).
The company may also adopt several authentication techniques for verifying the
identity of user involved in the processing of big data technology. Such practices in
authentication include latest biometric technology such as palm scanning, fingerprint and eye
scanning and so on. This will ensure double verification of the user data before providing
access to the server. In addition, secure communication in the processing cycle of big data
technology will also provide aid in supporting secure network. For instance, XYZ
organisation can deploy existing technology connectivity such as TLS/SSL that benefit the
company in protection of all network communication instead of subset of these networks. In
addition, the company is also required to deal with internal security as the company may face
in data security threat with its own employees. It is important for the company to conduct
background check while hiring employees and establish various communication security
policies in the organizations to upfront security protocols (Ekbia et al, 2015). This can be
proved to be a necessary step in enhancing data security among employees.
There are many sorts of encryptions technique also available in the market including
Triple DES, RSA, ECC, Blowfish and various other algorithms that may help the company in
protecting their data integrity (Cumbley & Church, 2013). These encryption techniques
convert the data into unreadable format considering third-party user. It is one of the most
cost-effective ways to the repositories presents in the big data technology. Considering
perimeter-based security, the data can better be protected by adding extra perimeters.

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For the purpose of strengthening the security, the company is also required to keep
constant check on the cloud service providers where the big data is stored so as to ensure
sufficient protection. With this periodic security and audits, the company can ensure that
adequate standards are fulfilled or not in extent with the security of big data. With regards to
this, the company can also set up various measures in real-time security monitoring to
prevent unauthorized access to the data and also for the usage of data. Even the threat
intelligence needs to be in place to make sure that more complicated attacks are found out
and that the enterprise can respond to this threat proactively.

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Conclusion and future trends
It is very important for the organisation to ensure that all big databases are exempt to
security threats and vulnerabilities while keeping up all the essential security protections such
as real-time management and consumer privacy. The advent of big data not only offer many
prospects to social growth and technology (AI) but also comes up with several threat
involved in hidden rules and patterns.
Considering future trends in Big Data solutions, it is very necessary to tighten its
security protocols and measures as its roots are expanded into various other applications and
technology such as IoT, data mining and Quantum computing. Many organizations are
jumping into providing better solutions and services to their clients by collecting vast amount
of data along with its means to manage and analyse. For instance, with smart devices like
Google, Microsoft Cortana and Alexa, it automates particular tasks and with the growing
craze for the technology demand industry to more investment in technological development.
Running these applications on ground of big data creates a playing field for the companies as
they have found most efficient way to integrate these into the business process.
Other than this, massive amount of data (big data) takes lot of time with the current
technology and so various companies are working on quantum computing that can crunch
billions of data in just a few minutes and gives enterprise an opportunity to create timely
decisions and hence achieve desired results.
All these technological leaps across different industries have solid foundations in the
form of big data. Future advancement will constantly help in forming a better society in
extent with the smarter process. It is not up to the organisation to adopt these changes while
addressing the relevant security risk.
As big data is in its early stage, it may be expected that with appropriate measures and
security solutions, benefitted can be transmitted to all users including organisation.

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References
Allen, A. L. (2016). Protecting one's own privacy in a big data economy. Harv. L. Rev.
F., 130(1), 71.
Andrejevic, M. (2014). Big data, big questions| the big data divide. International Journal of
Communication, 8(1), 17.
Chan, J., & Bennett Moses, L. (2017). Making sense of big data for security. The British
journal of criminology, 57(2), 299-319.
Cumbley, R., & Church, P. (2013). Is “big data” creepy?. Computer Law & Security
Review, 29(5), 601-609.
Ekbia, H., Mattioli, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T., ... & Sugimoto,
C. R. (2015). Big data, bigger dilemmas: A critical review. Journal of the Association
for Information Science and Technology, 66(8), 1523-1545.
Esposito, C., De Santis, A., Tortora, G., Chang, H., & Choo, K. K. R. (2018). Blockchain: A
panacea for healthcare cloud-based data security and privacy?. IEEE Cloud
Computing, 5(1), 31-37.
Kshetri, N. (2014). Big data׳ s impact on privacy, security and consumer
welfare. Telecommunications Policy, 38(11), 1134-1145.
Lu, R., Zhu, H., Liu, X., Liu, J. K., & Shao, J. (2014). Toward efficient and privacy-
preserving computing in big data era. IEEE Network, 28(4), 46-50.
Rubinstein, I. (2012). Big data: the end of privacy or a new beginning?. International Data
Privacy Law (2013 Forthcoming), 12-56.
Singh, J., Pasquier, T., Bacon, J., Ko, H., & Eyers, D. (2015). Twenty security considerations
for cloud-supported Internet of Things. IEEE Internet of things Journal, 3(3), 269-
284.
Spiekermann, S., Acquisti, A., Böhme, R., & Hui, K. L. (2015). The challenges of personal
data markets and privacy. Electronic Markets, 25(2), 161-167.
Tene, O., & Polonetsky, J. (2012). Big data for all: Privacy and user control in the age of
analytics. Nw. J. Tech. & Intell. Prop., 11(1), 27.

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Tien, J. M. (2013). Big data: Unleashing information. Journal of Systems Science and
Systems Engineering, 22(2), 127-151.
Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. (2014). Information security in big data:
privacy and data mining. Ieee Access, 2(1), 1149-1176.
Yang, K., Zhang, K., Ren, J., & Shen, X. (2015). Security and privacy in mobile
crowdsourcing networks: challenges and opportunities. IEEE communications
magazine, 53(8), 75-81.