Description
Submit a draft of Chapters 1 - 5.
Before submitting, you should review Chapter 2 to ensure that it aligns with Chapter 1 and 3. Are all of the variables in your research questions discussed in Chapter 2? Do you have literature in Chapter 2 that supports/justifies your research questions?
Review Chapter 1 and 3 to ensure that they align with Chapter 4. Do the research questions in Chapter 4, match the questions in Chapter 1? Did you make changes to your methodology as you worked on Chapter 4? Does Chapter 4 information match the steps discussed in Chapter 3?
Review Chapter 5. Does it align with the other chapters? Did you discuss the practical significance of each research question and tie in the literature from Chapter 2? Did you discuss if your findings for each research question was similar or different to the literature?
Explanation & Answer
View attached explanation and answer. Let me know if you have any questions.
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My Dissertation Progress
For my dissertation, I have made great progress in completing it. I have started on chapter
5, which will include the following sections: a summary of findings, practical assessment of
research questions, implications for future research, and limitations of the study findings. In the
chapter, I have included the practical significance of each research question and tied it to the
findings in chapter 4 and literature in chapter 2. Further, I will compare my results with those of
literature and how the differences affect my findings.
After completing chapter 5, I will review other chapters to ensure they align and all
sections described in the UC dissertation handbook are present and discussed. Further, for other
chapters, I intend to proofread for grammar errors and correct them, cross-check my references
and in-text citations that they align. Lastly, I will make necessary edits on each chapter based on
feedback from my supervisor to make sure I have a great write-up of my dissertation.
View attached explanation and answer. Let me know if you have any questions.
Updates 1-4
Based on feedback, I have made the following updates for my dissertation.
Chapter 3 changed the study method to qualitative and removed information on mixed
methods and quantitative methods. Further, I removed the statistical method
discussion and included the data analysis method, NVIVO. Also, I aligned the
sampling method and data collection method with chapter 4. In chapter 4, I have made
the following updates: the consent administration part, a discussion of how I achieved
credibility and dependability of the findings, and participants' response and practical
analysis of research questions. In addition, I moved all the figures and tables to the
appendices, included the consent and IRB form in the appendices, proofread all
chapters, and edited according to mistakes found.
View attached explanation and answer. Let me know if you have any questions.
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Chapter 5: Summary
Introduction
In review, the problem in this study was, big data has much potential, including deriving
insights that drive decisions and improve an organization's performance and productivity.
However, one significant concern with big data is privacy issues. Therefore, privacy protection
of information is paramount, and there is a need for technologies that will ensure it is
strengthened and maintained.
The purpose of this qualitative study was to provide the best framework that will reduce
vulnerability and protect user information to enhance new ways of data protection of the existing
systems by identifying current solutions and challenges surrounding big data management and
privacy issues. In addition, the research explored the existing security measures used by financial
institutions to enhance the privacy of big data. Further, the challenges institutions face with big
data management and privacy.
The qualitative study found that institutions have security protocol implemented on
various levels of data management to ensure user information is secure. Measures at each level
are significant to ensure data is safe from threats and unauthorized access. Another finding is that
one of the main problems that make user information vulnerable, inadequate staffing and tools.
Also, insider threats are another significant problem that institutions face. As a result, institutions
are trying to bypass the security challenges by being vigilant and strict on techniques they utilize
to keep user data safe.
Additionally, four themes were identified following data analysis: (1) Big data
management, (2) Data Privacy and Security, (3) Cybersecurity risk assessment and management,
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and (4) Regulatory mechanism for privacy. These four themes show the relationship between big
data management and factors that influence the privacy and security of data. The study's primary
aim was to recommend the best security measures institutions can implement to enhance privacy
when using big data.
Hence, this chapter will highlight the findings of the study. The chapter will include the
following sections: a summary of findings, practical assessment of research questions,
implications for future research, and limitations of the study findings. The practical significance
of each research question will be tied to the findings in chapter 4 and integrated with literature in
chapter 2. Lastly, an information security framework will be introduced, which emerged from the
study findings.
Practical Assessment of Research Question(s)
In chapter 1, the research questions were described that would guide the study. The first
research question was to identify the existing systems in banking institutions to secure user data
and prevent the threat to user information. Integrating the participant's responses and literature
culminated in the security model proposed that fulfilling each component enhances user data
privacy (See appendix C). With big data, the volume of available information collected by
institutions is massive. Hence, traditional approaches for data management are insufficient to
manage big data.
In chapter 2, several security frameworks are provided. The primary components of the
frameworks include techniques applied to enhance data security at different points of data
management: data generation, data storage and processing (Jain et al., 2016). In addition, the
frameworks are brief and clearly stated on what institutions should ensure they implement for big
data privacy. This study explored the existing system's financial institutions are utilizing to
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secure user data and prevent threats to user information deriving on the elements from the
frameworks.
Based on the study findings, institutions secure user data in three levels: Infrastructure
management, data security and regulatory mechanism. In infrastructure management, the
hardware and software utilized are essential in data management. Jain et al., 2016 proposes the
software used is crucial in securing user data. Institutions should use software that can restrict
access to data on networks. Also, the cloud infrastructure for data storage is essential to ensure it
cannot be tampered with or changed. Integration of several cloud types strengthens information
storage security from attackers and sharing without authorization (Elzamly et al., 2019).
Data security management involves using techniques and methods that ensure data is
secure. The study found methods such as access restriction and encryption methods protect the
data during storage and processing. Access control methods like authentication and encryption
safeguard information from unauthorized access. This method limits access without
authorization protocols being availed by a user. Authorization protocols include passwords and
fingerprints (Gwara et al., 2016).
Alone infrastructure management and security techniques are insufficient to enhance user
data privacy (Hasan et al., 2020). Rules and regulations enforced by the state and federal
government govern the application of big data. Protection of user data is crucial, and the rules
explicitly limit the extent personal information can be utilized and obtained. The Gramm-Leach
Bliley Act and California Consumer Privacy Act are laws that regulate the utilization of
consumer information by institutions. Zhang et al. (2018) point out that institutions are regulated
against sharing information with others and bear legal responsibility in case if the information is
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leaked. With big data, privacy information protection laws need to be better and robust to better
secure user data.
The second research question involved identifying the barriers and challenges banking
institutions to face to secure user data. The finds were institutions struggling to manage big data
effectively. The volumes of data keep increasing, and this increases the vulnerabilities that
implicate data privacy. Unavailability of adequate staffing and tools are threats to a robust
security network in institutions. Managing big data requires the appropriate tools and personnel
at every point of data generation.
Based on findings, financial information is the most targeted information. Zhang et al.
(2018) highlight that privacy-preserving mechanisms should target high-risk information to
enhance data security. The contact information is the last targeted information, and if the
information leaks, high-risk information is not comprised.
The data suggested, based on literature, themes identified, and answers to research
questions, solving big data privacy issues requires three levels of security measures (See
Appendix C, the security model proposed). On each level, there are elements that every
institution should consider. Enhancing data privacy requires data management, policies and
procedures and cyber risk management. Institutions utilizing this model maintain data security
and privacy of information collected and stored in their systems.
Limitation of the Study
During the research, the study encountered several challenges that impacted the
collection of data. This affects the findings of the research with a constraint to general
application practices. Lack of sufficient and effective data collection tends to temper the initial
objective of the study. The research encountered challenges in researching multiple financial
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institutions. The study participants were limited, implicating the availability of adequate
responses to answer the research question comprehensively. The study performed internal and
external validity in mitigating the anticipated challenge that emerged during the study.
One of the study limitations was the participant’s response to research questions. The
limitation included participants explaining the existing banking systems for big data in detail that
affects bank institutions. Time was a constraint as interviews were held over the telephone.
Furthermore, due to privacy concerns of data storage and production, the information was brief.
Finally, there was no verification procedure on how honest the answers were regarding the
interview questions.
Another study limitation was the participants' perception to what were the challenges to
secure data. The challenges have a broad extent in areas they can be found in an institution, and
hence, answers provided would be limited in the capacity of networks only. However, all the
participants were upfront and genuinely answered all the questions comprehensively.
Implication for Future Study
Professionals in the IT Industry
The professional IT industry and bank institutions face multiple challenges to secure user
data from the existing threats. The research offers a framework that allows banking institutions
to stay up-to-date on the latest technology management approach to secure user information. The
research highlights systems and methods that institutions can manage information in the financial
system. This includes an account to protect the customer's account with an approach to deal with
different challenges that the industry faces (Mehmood et al., 2016). This would include creating
a way of training bank institution personnel on data breaching and ways to reduce this disparity
that affects customers and bank institutions that offer services to clients. Furthermore,
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professionals need to create a forum for training other stakeholders to mitigate these challenges
in society. In addition, IT professionals should interact and connect with other stakeholders in
banking institutions to reinforce private concern management.
Banking Institutions
Understanding the proposal highlighted in the study help in maintaining the integrity of
financial institution in obtaining and utilizing user data. It is vital to understand the aspect of
methods and techniques to solve big data privacy concerns. The research provides an effective
way to enhance the security protocol to maintain big data privacy. The security model accounts
for new digital trends that lower the privacy concerns that affect most financial institutions. The
model reduces challenges based on personal data breaching. This help provides a permanent and
long-lasting solution that most government and bank institutions go through nowadays.
Recommendation of Future Studies
Although bank criminals have moved from physical robbery to cybercrimes, the security
framework approach will help solve this severe action of security breaches through guiding
financial institution to strengthening their systems. At the same time, it provides new
technological trends and ensures it offers customers the best services they deserve. Future
research should seek to provide a cloud computing internet-based approach to the rapidly
emerging issue that is a popular demand of service required in various institutions. The study
should focus on the popular demand of service and multiple organizations to structure semistructured data transferred for record and cloud server (Radwan et al., 2017).
The approach should seek to give recommendations on cloud computing to make sure
that personal information is safe and sound. Besides, include an approach to provide training to
all banking institutions on shifting and moving to the increased workload by using cloud servers
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that are safe to store user data and avoid access by third-party. This future research should
provide a perspective of review and analysis to the reason frameworks and architectural data
security that is continuously established against the threat and harassment and keep the stored
information in a cloud environment to avoid all the existing challenges today.
Summary
The security of existing data stored by institutions is an essential aspect for users who use
provide their information. The purpose of this research was to develop an information framework
to solve big data privacy issues. The methodology used in this research was the qualitative study
method. This approach helped to collect data from professionals in the IT industry working in
various financial institutions in San Antonio, Texas, to offer information on the best ways to
enhance big data privacy. Despite the study limitations, the methodology achieved collection of
information that was accurate and unbiased.
Although bank criminals have moved from physical robbery to cybercrimes, data privacy
is a concern that most institutions have failed to protect the customer from, thus leading to
adverse effects on institutions. The study purpose was fulfilled, and the framework provides the
best method to maintaining user data private so that user data cannot be accessed. It will help the
institutions and restore customer's confidence in the utilization of their data. The framework will
help institutions reduce the high cost of losing money to recover the debt and leaks and attacks
on their networks. The study recommends that institutions should adopt the framework to solve
big data privacy issues. Solving the issue facilitate the utilization and application of big data for
decisions making and innovation by institutions
.
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To save future generations from these long-lasting issues of breaching personal
information, the bank institution using the proposed framework protect user data.
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References
Elzamly, abdelrafe, Messabia, N., Doheir, M., Mahmoud, A., Bin Hasan Basari, Abd Samad,
Abu Selmiya, Nizar, & Sayed, A. A. (2019). Adoption of Cloud Computing model for
Managing e-Banking System in Banking Organizations - Journal of Alaqsa
University. Alaqsa.Edu.Ps. https://scholar.alaqsa.edu.ps/1144
Gwara, M. S., Okeyo, G., & Kimwele, M. (2016). A Framework for Assessing Cloud Computing
Security for Cloud Adoption in Microfinance Banks. International Journal of Advances
in Computer Science and Technology, 5(11), 151–159.
Hasan, O., Habegger, B., Brunie, L., Bennani, N., & Damiani, E. (2016). A Discussion of
Privacy Challenges in User Profiling with Big Data Techniques: The EEXCESS Use
Case. 2013 IEEE International Congress on Big Data.
https://doi.org/10.1109/bigdata.congress.2013.13
Jain, P., Gyanchandani, M., & Khare, N. (2016). Big data privacy: a technological perspective
and review. Journal of Big Data, 3(1). https://doi.org/10.1186/s40537-016-0059-y
Mehmood, A., Natgunanathan, I., Xiang, Y., Hua, G., & Guo, S. (2016). Protection of Big Data
Privacy. IEEE Access, 4, 1821–1834. https://doi.org/10.1109/access.2016.2558446
Radwan, T., Azer, M. A., & Abdelbaki, N. (2017). Cloud computing security: challenges and
future trends. International Journal of Computer Applications in Technology, 55(2), 158.
https://doi.org/10.1504/ijcat.2017.082865
Zhang, D. (2018). Big Data Security and Privacy Protection. Proceedings of the 8th
International Conference on Management and Computer Science (ICMCS 2018).
https://doi.org/10.2991/icmcs-18.2018.56
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View attached explanation and answer. Let me know if you have any questions.Hey let me know incase of any further revisions
INFORMATION SECURITY FRAMEWORK
Information Security Framework for Solving Big Data Privacy Issues
Nithish Reddy Arawala
DSRT 930
By
Name
A dissertation submitted in partial fulfillment of the requirements of the degree of
Ph.D. in Information Technology
At the
UNIVERSITY OF THE CUMBERLANDS
2021
ii
Signature page
This dissertation is my original work prepared and supported with credible sources and has not
been presented in any institution. It is submitted in partial fulfillment for the course DSRT930
Signature: Nithish Reddy Arawala…………
Date: 05/11/2021………………
APPROVAL
The undersigned certify that they have read and as a result of this recommend for acceptance of
Department PhD-IT, UNIVERSITY OF THE CUMBERLANDS a thesis proposal Information
Security Framework for Solving Big Data Privacy Issues
Prof.
Signature: ………………………………… Date:………………………………………………
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Acknowledgment
I wish to express my sincere gratitude to my supervisors for their guidance and support in
carrying out the dissertation. I extend my regards to all the staff members of the Department
PhD-IT for their moral support and encouragement during the dissertation period.
I thank my classmates, family, and friends for their moral support, financial support, and well
wishes. God bless you all.
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Abstract
Financial crime contributes to the societal ill and financial instability; thus, financial inclusion as
mitigation measures and prevention must be prioritized. As a concern, maximum effort needs to
be deployed on the law enforcement agencies to ensure legal and regulatory frameworks of
financial crime risk management tool kits are being deployed in all the financial and government
institutions to secure the user data. The framework proposed will provide additional security
control measures to improve and limit data utilization storage, among other useful content. All
banking institutions need to provide their customers with educational facilities to reduce
financial threats and detect any bank account attack. Employing brilliant employees and
customer care services will also reduce the risk of attacks and provide maximum protection to
the user information. An increased number of breaches and threats in most banking institutions is
an excellent example in San Antonio. These Texas banks need an immediate solution to secure
the institution's user data and bring back the client's trust in the banks. Most countries are
improving how they secure personal information with improved technology. It is imperative to
identify the current measures undertaken by a management team to secure data that need some
implementation and use the latest data security measures to protect user information in the bank
institution and government sector. The research requires a lot of time to collect accurate
information from the targeted audience. Limited access to information security and privacy
issues to data applied to big data applications were encountered. Explaining some exciting
solutions and problems that the research found helpful was an issue where some technical words
were not fully discussed.
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Table of Contents
Signature page ................................................................................................................................. ii
APPROVAL ................................................................................................................................... ii
Acknowledgment ........................................................................................................................... iii
Abstract .......................................................................................................................................... iv
CHAPTER ONE: INTRODUCTION ............................................................................................. 6
Introduction .............................................................................. Error! Bookmark not defined.
Background and Problem Statement ............................................................................................ 8
Overview of the Study ................................................................ Error! Bookmark not defined.
Purpose of the Study ................................................................................................................11
Significance of the Study ............................................................ Error! Bookmark not defined.
Research Questions ..................................................................................................................14
Definitions ..............................................................................................................................18
Limitations of the Study............................................................................................................20
Assumptions of the Study .........................................................................................................22
Summary ................................................................................................................................24
CHAPTER 2: LITERATURE REVIEW ...................................................................................... 27
Structure of banking systems .................................................................................................29
Big data...............................................................................................................................31
Big data analytics in the financial sector ..................................................................................34
Data privacy issues ...............................................................................................................41
Security frameworks for data privacy ......................................................................................42
Cloud providers....................................................................................................................44
Data processing ....................................................................................................................47
Big Data privacy-preserving methods .....................................................................................48
Privacy legal mechanism .......................................................................................................51
Security measures for the internet of things .............................................................................53
Related works ......................................................................................................................57
Conclusion ..............................................................................................................................58
CHAPTER THREE: RESEARCH METHODOLOGY ............................................................... 61
Introduction ............................................................................................................................61
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The Research Paradigm ............................................................................................................62
Research Design ......................................................................................................................64
Sampling Procedures and or/Data Collection Sources ...................................................................64
Data collection tools .................................................................................................................67
Statistical Tests of the Research ................................................... Error! Bookmark not defined.
Summary ................................................................................................................................74
CHAPTER 4: FINDINGS ............................................................................................................ 76
Research Setting ......................................................................................................................77
Participants .............................................................................................................................77
Analysis of Findings of Research Questions ................................................................................78
Additional Findings..................................................................................................................83
Big Data Management ..........................................................................................................84
Cyber Risk Assessment and Management ................................................................................87
Regulatory Mechanisms ........................................................................................................90
Summary ................................................................................................................................91
References ..................................................................................................................................... 93
Appendices .................................................................................................................................. 120
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List of Tables
TABLE 1: THEMES .......................................................................................................................... 87
TABLE 2: SEVEN RISK MODEL ...................................................................................................... 109
TABLE 3: AGE OF PARTICIPANTS ................................................................................................... 110
TABLE 4: YEARS OF EXPERIENCE ................................................................................................. 110
TABLE 5: INTERVIEWEES' PROFILE FROM FINANCIAL INSTITUTIONS ............................................. 111
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List of Figures
FIGURE 1: SCHEME FOR ADDRESSING BIG DATA PRIVACY CONCERNS ........................................... 112
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CHAPTER ONE: INTRODUCTION
Overview
Big data is rapidly changing the face of the global economy in the 21st century. Most
organizations have faced challenges in protecting customers' intellectual property and
safeguarding their personal information to maintain their confidentiality and ensure business
integrity and stability. As a result, security frameworks are being deployed to solve these data
privacy issues that will help most institutions safeguard the information.
Data privacy is a significant concern as most companies have failed to protect the
customer's confidential information, which is paramount in financial institutions (Ngdata, 2016).
The user interface provides powerful management to a single platform that relies on customers'
campaigns and uses a single platform that raises multiple IT services demanded by customers.
The information will respond to the customer's key life events, any behavioral changes that will
be taken are detected and provide maximum security on the data.
Using big data improves the intelligent judgment platform with your customers ensuring
your customers are well protected from information breaching or hacking. It is evident that with
the introduction of customer DNA, an institution needs to provide a clear understanding to their
clients on how the segment of their data is protected and is free from individual data breaches
and vandalism.
Artificial intelligence's power provides index recommendations on the best action to
delete conversation and customer individual experience. These offer a real engagement to the
customers and create awareness of advocacy and the best customer experience in storing their
information.
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Big data analysis will rely on customer DNA services to develop an advanced profile
with a sophisticated standard using CRM with these tactics. It will provide a single customer
view to ensure the populated customers cannot campaign and include structured data into the
internet and external sources (Lilley, 2018). As a customer organization, information is collected
and digitized based on potential analytics, cyber threats, and cyberattacks. As a result, large
amounts of consolidated information are required to protect customer information from attacks.
Financial institutions need to deploy intellectual protection services to safeguard user
information from breaches and threats that arise with this improved financial institutions'
technology. The existing innovation of big data relies on revenue generation and streams to
minimize life-threatening viruses and revolutionize the organization through the lengthy
investment of cybersecurity risks (Moreno et al., 2016). Financial action task forces need some
implementation to be applied with common compulsory national lottery regional determination
that constitute an offense and constitute the requirement of a common sanction approach to avoid
breaches.
Increasing financial logistics structures that will support anti-financial crime
organizations' domestic and multilateral public sectors will mitigate data breaching risks. These
logistics enhance building a better global framework that will fight financial crimes in businesses
and societal imperatives. Advancing in public and private partnerships is another leading factor
in ending these challenges. At the associated level, both financial institutions and law
enforcement agencies need to work together to protect the public from harm and crimes that
might arise (Tao et al., 2019).
Besides, Cybereason and scale are essential successful tools to secure big data. It is vital
to develop more strict rules and regulations to protect big data and reach global attention that will
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not be ignored without massive financial implications. These tools need to be modeled to allow
the collection of data and minimize end-user disruptions. This solution will provide statistical
analysis to machine learning and automatically adapt to the security environment changes.
Statistics demonstrate that revenue generation from 2014 to 2018 and the estimated next 20 years
will have more financial threat from cyber-attacks if this issue is not sold at an early age (Tao et
al. 2019). Also, risk appraisal and different methods need to be focused on cost-benefit to
compromise the best analytical models and describe the potential losses to the benefit for big
data and the users.
The economics of privacy encourages a proper collection of processed information and
stores them in a safer place to only be accessed with authorized users. Moreover, the economic
perspective analysis needs to ensure that cybersecurity economics has been minimized to protect
user information and ensure privacy issues have been deployed in all the banking and financial
institutions. It is imperative to identify the current measures undertaken by a management team
to secure data that need to be implemented and use the latest data security measures to protect
user information in the bank institution and government sector.
Background and Problem Statement
Big data bring a lot of conveniences to businesses for data-driven decision-making.
However, many institutions encounter inconveniences with big data. One of the inconveniences
is privacy. In the utilization process, if the methods available do not adequately offer protection
to user data, it threatens privacy. With big data additional to traditional privacy issues, utilizing
personal information to make analysis and research invade privacy. Institutions use mechanisms
such as anonymous identifies to hide identifier information of their customers when doing
analysis; however, this is insufficient as other contents can be defined accurately by customers.
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Institutions lack sufficient methods and processes hence the low adoption of big data by many
institutions.
This research aims to find ways to reduce the privacy issues associated with big data by
proposing an information security framework that will provide methods that will guide agencies,
including banks that face ransomware, cyber-attacks, and other threats in the world today. The
research generates customer confidence in all the financial institutions by suggesting the best
ways to reduce the scandals and pressure on customer personal data privacy standards. As much
as there are existing digital technology and big data privacy and security, these measures are
inadequate since the system still faces more threats, especially during the covid-19 pandemic.
The research problem discussed in the research is the significant application of privacy
issues, which is also associated with security protocol in implementing and handling user
information as most cyberattacks have been reported. Most organizations face challenges in
protecting customers' intellectual property and safeguarding their personal information to
maintain their confidentiality and ensure business integrity and stability.
An article by the Texas banking association highlights, in 2020, cyberattacks against
banks in Texas increased by 238% (Mills, 2020). There were increased wired transfer attempts
and ransomware attacks. The rise in attacks can be attributed to during the COVID pandemic, a
lot of the provider attention has shifted, and hackers utilize these opportunities to attempt attacks.
The rise in attacks adversely affects the application of big data. The research aims at identifying
current issues that affect big data management and private providers of frameworks that will
enhance an existing department that will minimize vulnerability and protect user information.
In most cases, financial institutions face information breaches as a lack of adequate
preparation to curb this issue (Security Intelligence Staff, 2019). As a concern, all the institutions
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need to implement the best ways to solve the problem, use reliable sources and implement the
best method of improving the internet of things to provide maximum security on user
information (Yang et al., 2019).
Research that needs to be emphasized is the privacy protection model based on the
multiple levels of trust system that would ensure low priority of data is compromised with a data
privacy periodic system's priority. The model will also entail the antivirus that will also wash
away all the cybersecurity challenges. The financial institution needs to develop frameworks that
will access all the computing environment's security levels before acquiring them to enable the
financial institution to evaluate cloud services and choose the best option.
Most financial and government institutions are facing critical threats to information risk
and the vulnerability of customer information. Different methods need to be deployed on the
ground to minimize these threats and make our financial institution safe to secure personal
information, like restricting possible loopholes that might attract money laundry. To ensure
maximum data protection and secure big data analysis, the diploid system needs to be strong and
concentrate on preventive measures such as antiviral firewalls and anti-malware applications.
The legal administrative management to access a control management panel will allow
users to turn on the best method to use and identify any problems. The frameworks provide
additional security control measures that will improve and limit data utilization storage, among
other useful content. All banking institutions need to provide their customers with educational
facilities to reduce financial threats and detect any bank account (Tsingou, 2020).
Employing brilliant employees and customer care services will also reduce the risk of
attacks and provide maximum protection to the user information. The technology used by
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hackers needs to be minimized, and find ways to ensure information access remarks are there to
all the prevention protocols put in place by the financial institutions.
All financial institutions should move with improved technology and invent new ways to
reduce all threats of securing and protecting data in real-time and protecting access control
methods of communication and inscription. It is vital to end script access of control methods to
data as most intelligent steps and storage devices are vulnerable. Trust zone-based solution
technology deployed maximum cloud computing protection to protect confidentiality as
developed by ARM (Hasham et al., 2019). Software-based data distribution control ensures
maximum security before sending a message to the users without limiting but providing an extra
description to the essential embedded software and sharing the whole software package deal.
Purpose of the Study
The purpose of this study is to provide the best framework that will reduce vulnerability
and protect user information to enhance new ways of data protection of the existing systems by
identifying current solutions and challenges surrounding big data management and privacy
issues. Suggested methods are likely to safeguard customer personal information, ensure
business entities, maintain the confidence, stability of stored information, and ensure business
continuity within institutions (Wagner et al., 2019).
The research will rely on different aspects to improve customer loyalty and confidence in
all financial institutions to fight cybercrime. The chosen qualitative research method will
compare other frameworks to determine the best of all the frameworks to ensure user data is safe.
The real-time big-time intelligence and analytics will help secure and provide visual patterns of
activities with the user information and learn from other mistakes to detect and prevent cyber
threats that are a challenge in big data.
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The research target is a population of employees working in banks in San Antonio,
Texas. The institutions are facing random cyberattacks that customer information is at risk.
During the pandemic, San Antonio bank in Texas is at risk of malware attacks, which is a
concern to suggest the best method to secure the institution (McGlasson, 2017). This population
was purposely chosen for this study to provide critical information on reducing information
breaches in the future.
On the other hand, all financial institutions must manage big data successfully and
maintain their security and compliance with the regulatory rules and progression rates.
Comparing the pharmaceutical industry and financial should be used to provide specifications
and experts on cyber criminals to peruse the health industry and suggest the best means
progressively.
The research suggests the adequate security issue that will protect financial institutions as
the existing system faces sophisticated and more frequent attacks. As a result, data privacy is a
significant concern today. A failure to protect our intellectual properties will lead to more severe
issues in the future that interfere with all our financial and government systems (Neville-Rolfe,
2016). However, the research will also entail specific methods to curb the malware attacks
targeting financial institutions and Banks.
IT experts are facing various challenges today. We also are identified and find out the
best way to help this IT expert overcome these challenges. Cybereason and fort scale are
essential tools that will differentiate features and capabilities to the targeted specific solution, for
example, and glowing of sensor and run in user space of endpoint operating systems that will
allow data collection and minimize end-user disruptions (Sullivan, 2016).
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With an increased Media coverage, the breach will be reduced at a high rate with other
suggested means of technology that will audit and commit the callers to rely on the end-user
expertise to reinforce authorized access to user information. It is imperative to note that the
implementation of data protection will only include specialized software tools and advanced
equipment that will polish the existing security measures and improve them to reduce all the
vulnerability that exists in our financial institution. However, an increase in media coverage
creates havoc for data security and increases the vulnerability of access to user information.
As a concern, to reduce these risks, more implementation of data protection should be
encouraged, and the use of software tools to develop and enforce policies and procedures that
will ensure maximum security on user information. All financial institutions should move with
improved technology and invent new ways to reduce all threats of securing and protecting data in
real-time and protecting access control methods of communication and inscription (Ullah & Ali
Babar, 2019). It is vital to end script access of control methods to data as most intelligent steps
and storage devices are vulnerable.
Future trends of the existing protective measures will also be discussed, and the newly
implemented ways will help solve these issues when they grow up to the exceeded level. With
the improved technology, new means of attacks will arise, and securing our financial institutions
with more protective measures needs to be deployed as early as possible.
Emerging economic perspectives to analyze different perspectives that protect big data
security privacy measures will also be discussed to improve security and minimize threats. In
most cases, most organizations face security problems as a lack of preparation for these harmful
attacks in advance. Despite all the existing property, the paper investigates an economic
perspective and analyzes all the protective measures of big data security and privacy.
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The risks of breaching and collection are favored by potential financial benefits such as
blackmail, false information, intellectual property theft, and business competition (El Alaoui et
al., 2018). They can be managed by the system at runtime with a set of components to ensure
optimal accuracy and response time. As a concern, economic investment needs to be motivated
to tackle all the cybercrime activities and analyze the privacy terms of economic perspective to
eliminate all the security threats today.
Research Questions
1. What are the existing systems in banking institutions to secure user data and protect the
vulnerability of information?
2. What are the barriers and challenges in banking institutions to secure user data?
Theoretical Framework
Big data security entails protecting user data when performing data processing.
Addressing privacy issues requires understanding provider perception and the use of systems in
offering protection to user data. The Communication privacy management (CPM) theory guides
an organization’s in identifying and protecting private information. This theory entails the best
way to disclose this threat and control the power of information beyond the management theory,
which is designed to consider people selected information and come up with criteria to handle all
the information to ensure ownership of data and management.
Communication Privacy Management is a system that “regulates disclosing and
protecting private information when others are involved” (Allen, 2017). Privacy refers to the
ability to determine when to disclose information and to what extent. The theory considers the
impact of the disclosure of private information. The theory explains the reasons people disclose
private information. Many consumers fear the lack of control over personal information (Yuliarti
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et al., 2018). The convenience of disclosing personal information is dependent on the assurance
that information will be safe and protected from any threats. The risk associated with disclosing
private information makes people vulnerable to exploitation (Allen, 2017).CPM suggests
boundaries should be placed to information to differentiate between public and private
information. The boundaries also control the accessibility to information and the expectations for
information use and disclosure.
Further, CPM suggests that individuals obtaining private information should develop
methods to protect privacy. Many authors have widely discussed the applicability of CPM to
solve privacy issues in organizations associated with technologies (Allen, 2017; Yuliarti et al.,
2018). CPM guides in privacy management in institutions help reduce theft, unauthorized access,
and malware attacks. The study will apply The CPM theory to understand ways institutions
develop and implement to protect the privacy of the user data they collect.
Communication Privacy Management is a system that “regulates disclosing and
protecting private information when others are involved” (Allen, 2017). Privacy refers to the
ability to determine when to disclose information and to what extent. The theory consi...