Security in Big Data Applications

MITS6011 / MITS6012

Advanced Research Topics in IT / IS

Security in Big Data Applications

Table of Contents

1. Outline of the proposed research 3

1.1 Background to the study 3

1.2 Purpose of the research 3

2. Rationale 3

3. Research topic and central research question 4

4. Methodological approach 4

4.1 Research design 5

5. Contribution 5

6. Proposed time schedule 5

References 7

Literature Review

According to the author (Zhang, D., 2018) data is a study through which the world could be changed. Big data is a set of several small data packets that together shape large data called Big data. everybody wants something everywhere in this dynamic era. Big data thus changes the habits of network monitoring protection methods. The data is now stored at a certain place and obtained from each site, so the big data is significant. However, since big data is quite easy for all to use and available, these advantages include some threats and this is the risk of technology. The paper would work on all of the mechanisms to solve the problems. The collection, preservation, and eventual use of data will result in data threats. As different challenges to security are dealt with and found in big data, various steps are required to mitigate these security threats. This paper addresses protection for big data and describes the security steps and the effect on the processing and handling of data from security threats. How to mitigate distractions and reduce risks the job can then be effective. We can find source information and the risks along with that, how one can resolve these risks to avoid the results. After many research articles and books articulating security knowledge in big data systems, this paper was published. The author also mentions that there is tremendous evidence in the digitization of everyday routine operations, which is big data. Big data is utilized for various reasons by any form of entity, but a similar mission is to support the client best. Organizations may make business decisions, monitor challenges or remedies, or identify risks. Different methods are used to analyze massive data and these methods are called analytics. Analytics are special and incredibly helpful, as massive data can be organized, semi-structured, or unstructured, modified, and analyzed using only analytical methods. The use of big data is quite straightforward and efficient, but each rose has thrones in the same way there are risks while using big data. and the explanation for this danger is the methods used to archive, process, and interpret data from multiple sources available. This paper would work on each of the mechanisms to solve the problem. The collection, preservation, and eventual use of data will result in threats to data. As a result, citizens become vulnerable as they are open to more material. Since the information can be obtained from various sources, it often points to breaches of protection and privacy, which is an offense. We ought to suggest robust security strategies that one has to connect to with safety and treatment, whether we access vast volumes of data without compromising privacy (Zhang, D., 2018).

According to the author (Jianqing et. al., 2017), big data offers digital society opportunities that are new and challenges which are new to data scientists. On one side big data has fantastic promises for the exploration of small-scale details of complex population dynamics and heterogeneities. Whereas, the sample size which is massive and big data’s high dimensionality, however, poses particular device and mathematical difficulties such as scalability and database blockage, noise aggregation, incorrectly correlated correlations, incidentals indigenousness, and errors in estimation. These problems are distinct and involve a modern model for programming and statistics. This paper presents an insight into the important characteristics of big data and how these characteristics influence the paradigm shift for the mathematical, numerical, and analytical approaches. We also offer many new insights into the study and measurement of big data. we stress in general the feasibility of the most fragmented solution with high confidence and find out that, because of unintentional endogeneity, exogenous conclusions are not validated in most statistical approaches of big data. they may lead to erroneous making analyzes and thus to inaccurate scientific results. The author states that big data offers unprecedented dimensions of scientific exploration and economic value. What’s fresh with big data and how are they distinctive from typical data of small to medium size? This paper outlines big data’s promise and problems, with a focus on the separate characteristics of big data and mathematical and calculational approaches, and computer architecture. Also, the author highlights that, when one enters the big data era, a term refers to an abundance of knowledge available. Such a large-scale data revolution is motivated by the thing that, vast quantities of very big and unstructured data regenerated and processed constantly at much lower costs than in the past. For example, the price of the whole genome sequence has dropped sharply in genomics. This also occurs in other fields including social media research, biomedical imagery, high-frequency investment, tacking videos research, and retail marketing. In the future, the present pattern in mass processing and storing in data will likely be maintained or intensified. This development would have a major influence on research, technology, and industry. For one, scientific advancement is increasingly data-driven and the researchers will see themselves increasingly as data consumers. The large volumes of data present new problems and possibilities to evaluate data. valid big data statistical analysis is becoming more and more relevant.

What are the priorities of big data analysis? According to this, two primary objectives of a data of high dimension study are to establish efficient methods to forecast potential findings reliably while at a similar time obtain evidence on the relationship among the characteristics and reaction for scientific purposes. In addition, big data leads to two other aims, given its broad samples: for getting the knowledge about heterogeneity and sharedness among various sub-populations. We recognize that confidential data is described as an obstacle in the privacy and protection of big data, which is a problem for business. There are several promises that big data make; (1) discovering each sub-populations hidden structures, which is normally not practical and may also be viewed as outliers because the size of the sample is very small; (2)extracting significant common features from a variety of sub-populations, a mid huge individual variation. The author implies that we need modern mathematical thinking and computing models to solve the complexities of big data. Many conventional techniques for modest sample sizes, for example, don’t scale up to large numbers. In the study of high dimension data, certain computational approaches that work well with low dimensional data often pose substantial challenges. We need to fix big data issues like heterogeneity, noise aggregation, false correlations, and unintended endogeneity in order to establish efficient computational methods to investigate and forecast massive data, in addition to balancing the accuracies of the statistics and machine performance. The author says they selectively review some specific showcasing in big data and explore several alternatives. This paper explores statistics and computational dimensions in big data analyses. Besides the difficulty of huge sample sizes and high dimensionality, big data is capable of equal treatment for many other essential characteristics. This involves (1) a dynamic issue of data: whereas big data are usually aggregated from several sources, they often show powerful behaviors based on tail. (2) data noisy challenge: big data typically involves multiple kinds of mistakes, outliers, and missed values. (3) the data problem depends on the tests: tests are based on comparatively poor signals in different forms of modern data, like financial time series, fMRI, and time microarray data.

Literature review

In the big data age, the lives, everyday activities, and ways of thought of people have endured earth-shaking transformation. Big data has been an important subject for business and academic study. Yet big data is a sword with double edges. It gives people comfort and offers customers the flexibility to other threats. Which can quickly contribute to the theft of personal information in the course of data gathering, storing, and usage & the fact that data is hard to distinguish? In the current process of study, how to maintain big data security & privacy protection has been one of the hot topics. This paper begins with big data, analyses big data protection challenges, & suggests defensive strategies security 7 privacy in big data. People are winners of internet technology in the era of big data. Data has a tremendous economic benefit for internet service providers, but it will be more complicated & difficult to handle the collection & processing of data, & personal privacy will be violated. With the increasing growth of the internet, individuals leave a lot of data trails on the internet every day. This offers offenders an incentive, not only for individuals, to gather data on the internet & then commit illicit acts such as reselling abuse, etc. life has caused difficulties & economic, severely undermining social peace & unity. How to deal with protection & privacy concerns in the form of big data is an important need for individuals to provide a reasonable approach in the age of big data. The internet is the root of big data. Researchers create diversified models depending on the company’s requirements & then drive concrete variables to suggest ways to interact with individuals or items in various situations depending on the models. This is the origins of big data & its characteristics. Big data can be classified into three groups according to origins of big data: all sorts of data coming from users, users using the internet, including video, photographs, email, etc. data obtained during the operation of different types of digital equipment, e.g. data signals captured by the camera. This kind of ecosystem has been increasingly important for the protection of mobile data in terms of intelligent data terminals since the advent of the big data age and the exponential rise of the internet. China is currently the biggest smart mobile terminals development market in the whole world. This vast numbers of mobile devices consume not only the resources and time of citizens, and also externally retain more personal information. At the moment, the protection of big data is badly impaired and big data is not secure.

Knowledge is in the age of big data spread in a very fast rhythm. Although the flow of information does not have very high importance and data is diminished due to inadequate data oversight, the lack of technological assistance, insufficient management mechanism, and the insecure loss of information. The validation would have several detrimental consequences and contribute to higher economic results for people, corporations, and even culture. many new insights on the study and measurement of big data. They stress in general the feasibility of the most fragmented solution with high confidence and find out that, as of unintentional endogeneity, exogenous conclusions are not authenticated in most statistical approaches of big data. They may lead to mistaken making analyzes and thus to imprecise scientific fallouts. Not just the big data problem. Still very troubling are the security issues of smart terminals. There is still a significant issue for consumers with the security of smart terminals. Intelligent items often develop from existing personal smart terminals to intelligent residences. The personal smart terminal of the user will later monitor the function of the home terminal. Then whether one monitors the function of the home terminal. Then whether one monitor or lose anyone’s mobile terminal, it can create severe security issues for the smart home of the customer. In addition to creating tremendous opportunities for social change, the rise of the age of big data has introduced numerous risks to the confidentiality of knowledge to society, making the preservation of privacy a priority. To ensure that information on big data is protected and hidden, it is important to raise awareness of the rights of privacy of the citizens of our country, as well as a vast number of competent private information management technology, so the secrecy information safety can be enforced.


Zhang, D., 2018, October. Big data security and privacy protection. In the 8th International Conference on Management and Computer Science (ICMCS 2018). Atlantis Press.

Abouelmehdi, K., Beni-Hssane, A., Khaloufi, H., and Saadi, M., 2017. Big data security and privacy in healthcare: A Review. Procedia Computer Science113, pp.73-80.

Fang, W., Wen, X.Z., Zheng, Y. and Zhou, M., 2017. A survey of big data security and privacy preserving. IETE Technical Review34(5), pp.544-560.

Yin, C., Xi, J., Sun, R. and Wang, J., 2017. Location privacy protection based on differential privacy strategy for big data in industrial internet of things. IEEE Transactions on Industrial Informatics14(8), pp.3628-3636.

Fan, J., Han, F. and Liu, H., 2014. Challenges of big data analysis. National science review1(2), pp.293-314.

Leonelli, S., 2019. Philosophy of Biology: The challenges of big data biology. Elife8, p.e47381.

Weersink, A., Fraser, E., Pannell, D., Duncan, E. and Rotz, S., 2018. Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review of Resource Economics10, pp.19-37.

Gulgec, N.S., Shahidi, G.S., Matarazzo, T.J. and Pakzad, S.N., 2017. Current challenges with bigdata analytics in structural health monitoring. In Structural Health Monitoring & Damage Detection, Volume 7 (pp. 79-84). Springer, Cham.

1. Outline of the proposed research

1.1 Background to the study

The concept of big data has gained significant precedence in the recent years even though it has been in existence for a very long time. This is because many business organisations have realised that together with the help of analytic means, they can obtain a significant amount of value by capturing the data streams concerning their business operations [1]. Thus, big data analytics can bring to the table numerous benefits such as improved efficiency and speed that can eventually help with the intense decision-making processes. Thus, this newfound agility and ability to work in a faster manner enables organisations to have an improved form of competitive edge over the market rivals, which they previously lacked.

However, just as in the case of any new form of connected technology, big data too has many issues that could arise regarding its security and privacy aspects [2]. Big data security typically refers to the act of safeguarding the collected data along with the analytics processes from those issues that could essentially compromise the confidentiality of the information. The primary problem that lies here, is that the information that has been collected is vast, and thus, the opportunities of a situation arising wherein a data breach occurs is far greater as compared to the traditional approaches [2]. This further brings to the table the discussion on the economic investments that an organisation would have to make in order to ensure the privacy and security of the systems.

1.2 Purpose of the research

The aim or purpose of this research is to understand the economic perspective in terms of the investments that a business organisation would have to make to ensure that its big data applications and analytical processes are safe and secure from the threat of cybercrimes and frauds. The goals or objectives of this research are –

1. To understand the various threats and security risks regarding big data applications

2. To understand the economical aspects of implementing a big data security system to safeguard against fraudulent and phishing activities.

2. Rationale

The reason as to why this research is being conducted is because big data analytics is concerned with the handling of a significant amount of data and information, which indicates that the security issues would also be equally massive and intense. Thus, effective electronic data storage systems need to have an adequate level of security to safeguard the information they collect, since the data is often sensitive in nature and any breach could lead to massive problems [2]. The investments that organisations can make for this purpose is also varied since not all businesses are equally equipped with the resources to compensate for the same. Thus, developing an understanding of the economic parameters and perspectives of big data security is an important point that needs to be explored and understood very clearly.

Understanding the financial aspect of big data application security is important to those individuals and organisations who are regularly engaged with such analytical processes with regard to the businesses they run or are involved with. This is because businesses need to be aware of the investments they need to make if they wish to incorporate big data analytics and related applications and technologies into their processes. Thus, having a clearer perspective towards the economic aspects of this issue can enable them to make better decisions while considering the best interests of the business organisation.

3. Research topic and central research question

The research topic as chosen for this particular study is privacy and security in big data applications, and the economic perspectives related to it. Based on this topic, the central research question of the research is –

What are the economic perspectives of big data privacy and security?

4. Methodological approach

The methodological approach typically refers to the tools and techniques that are adopted by researchers to address the questions and goals as indicated by a study. As far as the paper titled “Economic perspective analysis of protecting big data security and privacy” is concerned, the authors seem to be have adopted a qualitative approach, which they have fulfilled through the use of a case study analysis. Two comparative studies have been consulted, along with a case study on the Department of Veterans Affairs [3]. Thus, this methodology brought with it the problem of being limited to only a short amount of case scenarios, thereby making the results irrelevant to a wider audience. Thus, in order to rectify this issue, this particular study shall adopt a quantitative approach by surveying at least 10 SMEs wherein the focus of the data collection technique would be to understand how their spending patterns and business costs have been affected ever since they implemented the use of big data analytics. It can be estimated that the quantitative approach shall be more applicable here since a more accurate account of the costs and benefits of implementing and using big data applications since they require adequate investments to ensure the privacy and security of the data. The data as collected shall be utilised to understand the economic perspectives associated with providing security to big data applications in a more scientific manner since it would be quantifiable and hence statistically processable.

4.1 Research design

For the purpose of this study, the required data shall be collected using a questionnaire with at least 10 close-ended questions. This shall allow for less ambiguity and ensure that the data collected is accurate and therefore appropriate for the research purpose. In contrast, the research design of the paper that has been consulted for this study has a very empirical approach and the short pool of cases analysed limits its applicability to a wider context.

In order to collect the required data, 10 distinct SMEs shall be selected, typically those who already have implemented the use of big data analytics in their business processes. The questions shall be aimed at understanding the decrease or the increase in their spending with regard to the security and privacy upkeep of the data systems. This data as collected shall then be analysed using SPSS to give rise to a set of verifiable and concrete results.

5. Contribution

In terms of the significance of the research, the results as obtained in the end shall ultimately help with understanding the economic aspects of implementing a privacy and security measure for big data applications. The study shall definitely fill in the gaps when the existing researches are concerned, since the paper as consulted takes into consideration only a limited number of case studies, thereby rendering the outcomes inapplicable to a wider context. The use of a more holistic quantitative approach as in this research study shall solve this issue and provide a more concrete set of evidence that business organisations can consider when looking to implement big data applications and the related security measures.

6. Proposed time schedule

For the purpose of better understanding the timeline as to why this research shall be conducted, a Gantt chart as indicated below has been proposed.

Table 1: Gantt chart denoting the research timeline

Task / Activity Sep Oct Nov Dec Jan Feb Mar
Finalising the research topic

Finalising the list of SMEs to survey

Finalising the questionnaire

Preliminary literature review

Emailing the company representatives to notify them

Submission of consent forms from the representatives

Emailing of questionnaire to the SME representatives

Data collection and compilation

Data analysis

Documentation of the outcomes

Preparation of publish report

Writing of final report and submission

(Source: Author’s creation)


[1] A. Urbinati, M. Bogers, V. Chiesa and F. Frattini, “Creating and capturing value from Big Data: A multiple-case study analysis of provider companies,” Technovation, vol. 84, pp. 21-36, Jul. 2019.

[2] F.Z. Benjelloun and A.A. Lahcen, “Big data security: challenges, recommendations and solutions,” in Web Services: Concepts, Methodologies, Tools, and Applications. Pennsylvania: IGI Global, 2019, pp. 25-38.

[3] H. Tao, M.Z.A. Bhuiyan, M.A. Rahman, G. Wang, T. Wang, M.M. Ahmed, and J. Li, “Economic perspective analysis of protecting big data security and privacy,” Future Generation Computer Systems, vol. 98, pp. 660-671, Mar. 2019.

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