Big Data Security Concerns

This report analyzes the background of big data security concerns, evaluates privacy concerns, discusses the pros and cons, and investigates potential solutions.

Running head: BIG DATA SECURITY CONCERNS
Big Data Security Concerns
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Executive summary
The big data like other types of cyber security is a variant. This is also concerned with different
attacks. It has been coming out if various online and offline arenas. In this report, the background of
the big data security is analyzed. It is demonstrated on the various topics as per the 5W model. Apart
from this, the concerns of privacy within the security of big data domain is evaluated. Then, the pros
and cons of the innovation security is discussed. At last, the effective resolutions for securing the big
data are investigated in the study.

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Table of Contents
1. Introduction:......................................................................................................................................3
2. Background of the topic:...................................................................................................................3
2.1. Evaluation of the current topic on the basis of 5Ws:..................................................................4
3. Privacy concerns in Big data security:...............................................................................................6
4. Security concerns Big data security:..................................................................................................7
5. Big data security- Pros and Cons:......................................................................................................9
5.1. Understanding the pros:..............................................................................................................9
5.2. Cons of big data:.......................................................................................................................10
6. Potential Solutions:..........................................................................................................................10
7. Conclusion:......................................................................................................................................11
8. References:......................................................................................................................................12

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1. Introduction:
The “big data security” is a collective term. This is regarding all the measures and tools. This
is used for guarding the analytics and data processes. This involves the theft, attacks and additional
malicious tasks. This can harm and negatively affect that.
Similar to other kinds of cyber security, the variant is also concerned with various attacks.
This has been originating from the offline and online spheres.
In this study, the background is demonstrated. This is evaluated on the topics on the basis of
5Ws. Further, the privacy concerns under the big data securities and the pros and cons of big data
security are evaluated. Lastly, the potential solutions are demonstrated here.
2. Background of the topic:
The term “Big Data” is been coined for describing the massive amount of semi-structures and
unstructured data. This has been including the three properties. This has involved the velocity,
variety and volume that is considered as the example. Here, this involves the quantity of data,
varieties or the complicacy of numerous data types. The velocity involves the data in motion. This
involves the rise in data produced and this data is defined as the “Big Data” (Zhang, 2018). The big
data architecture has been distributive in nature. This scales to thousands of information and the
nodes of processing. All the nodes, the information is partitioned, distributed and replicated for
powerful computation. This is due to the reasons of performances. Here, the data is also been
segmented to different classes. It also features the real-time processing, auto-tiring and streaming of
data. It includes the important trends under the big data assessment (Lafuente, 2015). Moreover, the
rising number of business has been utilizing the technology. This is to analyze and store the

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petabytes of information. This involves the social media contents, clicking the stream data and web
logs. This is helpful to achieve the smarter insights regarding the business and customers.
2.1. Evaluation of the current topic on the basis of 5Ws:
The 5Ws are demonstrated hereafter.
What:
There has been the myriad attack of various attacking measures. This targets various
weaknesses. Hence, this has been vita for pinpointing exactly that has resulting the incident. Further,
the defacing web sites has been falling out of the fashions. This is in favor of the data theft and
Ransomware. For instance, the DDoS attacks has been directly targeting the digital infrastructure of
the company. This has been indirectly targeting the service providers. This has been also a rising
issue. Apart from this, the attackers has been also beginning to place mass attacks of data
destruction. This has been also seriously affecting the business (Manogaran, Thota & Kumar, 2016).
When:
Knowing the timing is the part of creating the better scenario of the incident. Here, there are
no holidays for the worldwide hacking community. However, there are specifically savvy attackers
that has been meaningfully engaging the cyberattack (Hashem et al., 2015). This has been during the
periods of national holidays as they come to understand about the personnel of security. This is
short-staffed and then over low-alert. Here, the timing is also essential element for considering as
one requires to notify the partners of the business and the consumers. This states that the information
is compromised.

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Where:
The most vital query arguably is to response to different attacks and breaches. This indicates
the place where it has been targeted. It has been including the detailed review of the overall surface
of attack. They should be considering the network, remote staffs, partner’s, suppliers and the
affected USB stick that must be blamed (Hu & Vasilakos, 2016). At present the essential common
point of entry is the email. For the hackers they have been crafting the attacks of phishing for
targeting the weakest link under the chain of security and the end users.
Why:
Here, the main aim of the attack has been a vital piece of data. This is for the external
announcements required to be done. Possessing the details has also been useful as the justification of
the plan f incident reaction is considered. This also involves the recommending of the extra security
to spend to the executives of the company (Xu et al., 2016). Here, the economic motive for most of
the part has been the top most cause for the attacks against the business. Though the attacks that are
state-sponsored, has been driven economically for some sense. It has been costing years with
millions of pounds for developing the intellectual resources and the base of customers that is been
stolen in just hours.
How:
For undertaking effective remediation, one requires to generate a step-by-step detailed
outline. This must involve exactly the ways the hackers has been attacking and breaching the
business. Here, the tactics are also evolving and the older tricks has also been facing a comeback.

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3. Privacy concerns in Big data security:
Filtration and validation of the inputs of the end-points:
The devices at the end point are the greatest factors to maintain the innovation. The
processing, storages and other important activities are done through the help of input data. This is
delivered by the end-points. Thus, the business can assure the legitimate and authentic devices of
end-points.
Securing the calculations of distributed framework and additional processes:
The computational security and additional digital resources are under the distributed system.
This involves the MapReduce activity of the Hadoop. This is mainly the due to h lack of security
protections (Wolfert et al., 2017). The two main preventions for it are securing the mappers and
protecting the data in the presence of an unauthorized mapper. The huge quantity of the data
generation has led to the sustaining the regular checks. Nonetheless, this is the most advantageous to
undertake the security observations and checks under or almost real time.
Securing the access control method encryption and communication:
The protected device of data device is a vital step for protecting information. However, sue to
the common devices of data storages that are vulnerable, this has been required to encrypt the
methods of access controls also.
Data provenance:
For categorization the data, it has been required to become aware of the origin. It is useful for
finding the origin of data accurately, validation and authentication and thus the access control can be
achieved.

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Granular auditing:
It is helpful to analyse the various types of logs. This is beneficial and the data is also useful in
identifying the type of the malicious tasks and cyber-attacks. Thus, the constant auditing is also
beneficial (Chin, Li & Chen, 2017).
Granular access control:
This for the stores of big data by the NoSQL databases and the System of the Hadoop
Distrbuted File has needed important process of authentication. This has also required a mandatory
control of access.
Privacy protection for the non-rational stores of data:
Different stores of data like NoSQL has also been bringing the security vulnerability. This
has also caused the threats for the privacy. It has involved a prominent flaw in security. They have
bene unable to encrypt the information. This is during the logging and tagging of data. This must be
done through distributing that to various groups as it is collected and streamed (Terzi, Terzi &
Sagiroglu, 2015).
4. Security concerns Big data security:
Lack of designed security:
The big data has been providing the business capabilities that are never been accessed prior.
It is one of the primary draws to use the huge quantity of data. It indicates that various platforms has
comprised of encryption, compliance, risk management, policy enablement and so on.

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Anonymity concern:
Various clients has been feeling uncomfortable with the concept of the business This is to
collect the detailed data about the sensitive facts, motivations, behaviours and identities.
Diversity has been complicated:
The more complicated the data sets, the more complicated they have been for protecting. The
diversity of the big data has been coming from various areas. The forms of data has been both
unstructured and structured (Moreno, Serrano & Fernández-Medina, 2016). Further, it has been
coming from the email file, servers’ applications of clouds and data of mobile devices and so on.
The consumers of the data has been anyone from the high-level executives for the business users and
consumers. Much more diverse data indicates that the tasks are required to be protected.
Data breaches are common:
There are various data breaches that has been happening currently. Huge quantity of data like
goldmine for the cyber criminals and organizations has been collecting that storing that. These are
big targets.
Spending on security has been lower:
One can think of the rise in cyber-attacks indicating the rise to spend the IT security for
protecting the big data. However, that has not been case always. Here, most of the experts has been
agreeing across 10% of the IT budget of the business. They should be appending on the security with
the average under 9%. Despite the resources of requisite, the business has been seeking that
complicated for protecting the data of the company.

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Gap of big data skills:
Resolving the security concerns has been probable with restricted resources as the proper
people are there for the job on that. However, the business has been experiencing for the gaps of big
data skills (Abouelmehdi, Beni-Hessane & Khaloufi, 2018). Moreover, there are just less individuals
who are the data scientists and data exerts over the staff. They have been making that more
challenging for addressing the shortcomings of security. It is complicated for seeking the proper
people having the proper skills for handing the tasks. This has been rising the significance of the
concerns.
5. Big data security- Pros and Cons:
5.1. Understanding the pros:
 Better decision making:
o The analytics can provide the business decision makers with the insights that are data
driven. This is helpful for the companies in competing and rising.
 Developed customer service:
o The CRM systems and the social media and the other points of customer contacts has
been providing enterprises the wealth of data. This is for the customers.
 Fraud detection:
o It provides the credit card companies and banks the ability to spot the stolen credit
cards and fraudulent activities.
 Rise in revenue:

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o As the business has been using the big data to develop the decision making and
develop customer service, there are rise in revenue for the natural results (Wang,
Kung & Byrd, 2018).
 Rise in agility:
o It is useful to support quicker and frequent changes in business tactics and strategies.
5.2. Cons of big data:
As the organizations fail to address the issues with data quality, they might find the insights
generated by the analytics to be worthless and harmful.
 Cyber security risks:
o The storing of big data, particularly the sensitive one has been making the companies
to the attractive target.
 Complicacy to integrate the legacy systems:
o Integrating the disparate source of data and moving the data has been needing more
time. This has involved the expense of working the big data.
6. Potential Solutions:
 Keeping a check on the cloud providers:
o It must be fetched that the provider makes periodic audits of security. They should be
agreeing on various penalties in the case as the enough security standards are never
met (Shoro & Soomro, 2015).
 Adequate access control policy to get created:

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o The policies must be done as they permit the access to authorised users. This prevents
the unauthorised access to information from the external and internal sources (Zhou
& Luo, 2017).
 Real time security monitoring:
o In order to prevent the unauthorised access, threat intelligence must be utilized.
7. Conclusion:
The big data platforms has been featuring greater correlation of data. This can be used for
predicting and understanding the attacks that are planned in long terms. In future one can assess the
real tie logs with the older logs for understanding the attacks. Moreover, the integration with
networking devices and security devices can deliver the link usage management and network health.
Again, the big data analytics on the network or endpoint infrastructures are able to find the
anomalous tasks within the network. In future the real time intranet tasks are been correlated with
the older reference traffic. This is to detect various malicious tasks under the network. Lastly, the
compromises endpoints are to be found out and then it is isolated from the network. This is prior the
infection has been spreading the systems under the network.

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8. References:
Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: preserving security
and privacy. Journal of Big Data, 5(1), 1.
Bunnik, A., Cawley, A., Mulqueen, M., & Zwitter, A. (Eds.). (2016). Big data challenges: society,
security, innovation and ethics. Springer.
Chan, J., & Bennett Moses, L. (2017). Making sense of big data for security. The British journal of
criminology, 57(2), 299-319.
Chin, W. L., Li, W., & Chen, H. H. (2017). Energy big data security threats in IoT-based smart grid
communications. IEEE Communications Magazine, 55(10), 70-75.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of
“big data” on cloud computing: Review and open research issues. Information systems, 47,
98-115.
Hu, J., & Vasilakos, A. V. (2016). Energy big data analytics and security: challenges and
opportunities. IEEE Transactions on Smart Grid, 7(5), 2423-2436.
Lafuente, G. (2015). The big data security challenge. Network security, 2015(1), 12-14.
Manogaran, G., Thota, C., & Kumar, M. V. (2016). MetaCloudDataStorage architecture for big data
security in cloud computing. Procedia Computer Science, 87, 128-133.
Moreno, J., Serrano, M., & Fernández-Medina, E. (2016). Main issues in big data security. Future
Internet, 8(3), 44.

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Shoro, A. G., & Soomro, T. R. (2015). Big data analysis: Apache spark perspective. Global Journal
of Computer Science and Technology.
Terzi, D. S., Terzi, R., & Sagiroglu, S. (2015, December). A survey on security and privacy issues in
big data. In 2015 10th International Conference for Internet Technology and Secured
Transactions (ICITST) (pp. 202-207). IEEE.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and
potential benefits for healthcare organizations. Technological Forecasting and Social
Change, 126, 3-13.
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review.
Agricultural Systems, 153, 69-80.
Xu, Z., Wu, Z., Li, Z., Jee, K., Rhee, J., Xiao, X., ... & Jiang, G. (2016, October). High fidelity data
reduction for big data security dependency analyses. In Proceedings of the 2016 ACM
SIGSAC Conference on Computer and Communications Security (pp. 504-516). ACM.
Zhang, D. (2018, October). Big data security and privacy protection. In 8th International
Conference on Management and Computer Science (ICMCS 2018). Atlantis Press.
Zhou, Q., & Luo, J. (2017). The study on evaluation method of urban network security in the big
data era. Intelligent Automation & Soft Computing, 1-6.

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