Literature review on data security and privacy in analytics
This sample is about data security and privacy and also flow chart, algorithm and business model. This is a literature review and after getting through this sample, you will understand how data security and privacy in analytics plays a vital role in business organization. The sample credits goes to one of the academic writer working with Myassignmenthelperonline.com. You can use the litearture review given below as a reference for writing your dissertation.
Aim of this report is to demonstrate the concern that occurs due to data security and privacy in analytics for any business organization. This also shows the impact of misuse of the data algorithms, flow chart, and business model on the business of a company.
Various authors have done the research to evaluate the issue of data security and privacy concern in data analytics but they could present impactful evidence to support their research. A major objective of this report is to present the pieces of evidence to demonstrate the extreme impact of data security and privacy issue in data analytics.
The scope of this report is to describe the issue data theft or misuse of business-related assets that can bring the entity of the company at stake. It is important to provide security to the business-related data to retain data privacy for a long time.
A major issue of fake data generation always accounts for increasing the vulnerability in data security and privacy. Ahmed et al. commented that there are some cybercriminals who can fabricate the business-related data to pour into the organization's data lake. It disrupts the system of data analysis . For example, Dixon Carphone faced the issue of data breaching of 5.9 millions of bank cards and personal data of the customers. Criminals intruded into the servers, fabricated the large volume of data and replaced that information with the information of bank data server. As s result, the organization faced mismatch in data analysis of the customers as their fake names and address were showing there. Therefore, fake data generation can be the most treacherous issue in data analytics. On the contrary, the trouble of cryptography protection is the most sensitive issue that can handover huge loss to the company. Steiner, Kickmeier-Rust, & Albert, LEA in private argued that most of the organizations keep the business data encrypted for strong data security . This encryption process performed on the cloud computing platform. If an organization does not put the effective key on the encryption then data can be hacked and competitors can misuse the data for their business purpose. Continuous encryption and decryptions of a large volume of data slow down the speed of the operation that consumes a huge time to make an analysis of data. The biggest example to support this incident is the FaceBook accounts hacked by Nigerian company to handover the data to an insurance company. Millions of encrypted data were hacked and transferred into the server of an insurance company for calling the people for a loan in the country. It has emerged an issue inside the customers for using FaceBook on a large scale.
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In the evaluation of the data security and privacy in data analytics, Mantelero evaluated that penetration Perimeter security is the most vulnerable issue that can be costly for a company to arrange the business data like sales report and tender calculations . In Perimeter security all points of entry and exit remain closed. It will also not give any information about the tasks performed by an IT specialist in the system. Therefore, If IT specialist is corrupted then they can easily change the business data statistics of tenders and sales report secretly even upper management cannot get aware of it. Wrong data analyses because of wrong statistics can handover a huge loss of assets. In the USA, nearly 10 construction companies faced a huge loss by losing the tenders as their data were manipulated by their employees by misusing the perimeter-based security. On the contrary, single-layered authentication and given authority to every official for data access is the most burning issue because of that most of the organizations are suffering data privacy and security issue in the analysis work.
Lee argued that some organizations do not follow the instruction of Cyber Security Cell, USA and give permission to every staff for accessing the data that increases the issue of the data security on a large scale. According to Section 2701 of the Stored Communication Act of Cyber Security Law, only a responsible upper management person should have the right to access the data of the customers for the business purpose only. If data is lost or stolen by the third party then a person will be responsible to bear the penalty for this un-procedure work. For example, in 2018, the US Government cancelled the license of several small IT ventures that had vulnerable data management process occurred the issue false business report in the data analytics process.
In the evaluation of data privacy, Lee opined that high speed of evolutions NoSQL database is the major threat that can emerge the situation of data privacy and security in the business data analytics process . NoSQL database is continuously getting the new updates of the features and therefore, in this situation, the security package system gets mistreated and gives the poor security for the data in the new improves system channel. The improved and upgraded database also needs upgraded software for better security support. This vulnerability gives lots of the third party to intrude into the database to manipulate or reverse the business data statistics which can give the error in the result at the time of the data analysis process. For instance, in 2019, several data of HCL customers and employees, business project data, and sales report analysis have been exposed online because of the third party attack on the database server. Even the database was protected by security software; the third party used the anti-encapsulated security breaker program that was enough to neutralize the security package of the HCL database. Though the database was based on NoSQL Platform, continuous updates made the current security package less effective to protect the entire server points of the database.
On the contrary, Mantelero argued that the presence of untrusted mapper is the most concerning issue that can create the vulnerability in data privacy and its security. Once business data is collected, it is sent for parallel processing where MapReduce Paradigm is followed . When data is broken into several chunks to perform the data analysis work, mapper processes and allocates them with storage options. If a third party has to access the code of the mapper then it can change the setting of existing mapper or can include the ‘Alien’ ones. Thus, following this method one can ruin the entire data processing effectively. A major problem behind that is some of the databases like Big Data does not provide an impactful and additional security layer for the protection of the data. It remains relied on the perimeter security systems. It creates a possibility of data theft. A company can lose the data and Business analyses with a representation of the data can restrict. In 2018, Yahoo had to face the issue of the account destruction as the third party used the same process to intrude into the mapper system to access and change the code of the web database. Lots of accounts were destructed by the third party. There were many cases of Yahoo’s business data leakages that exposed all the business data analysis in front of its competitors. As a result, several new business operations in Yahoo were restricted to be performed in the country.
In the contradiction of data privacy concern in the data analytics process, Lee opined that less access control has been the headache for several companies in the country. Most of the companies do the data representation and analysis work connecting to the database packages . This database package remains quite vulnerable and tends to expose all the data analysis to the third party. The multilayer authentication and multilayer server protection are not provided to the database packages. As a result, competitors of the business can attack the web servers and break the code of single-layer protection to access the database. The company also needs to increase the access control on the database servers so that if one access control is blocked by their party then another access control can be used to reach to the centralized point of the database. In 2019, Google has used the multiple access control in the GOOGLE+ data analysis applications so that if the third party intrude in the data application of the company then it can be neutralized by sending the command using alternative data access controls in order to avoid the data security and its privacy.
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By reviewing all the information regarding the issue of data privacy issue and data security it can be evaluated that data analytics is the process where all the graphical representation, data mapping, and business reports are represented by using the information of the database. It can be risky for any organization if data security and privacy are manipulated by illegal activity in the business. Use of manipulated data contains false information and can give a wrong analysis of business data. Customers and employees entity can be a stake if the data gets leaked. Therefore, it is quite mandatory to keep the data secured by using authentic software security.
Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. et al., “The role of big data analytics in Internet of Things,” Computer Networks, Vol:129, pp.1-10, 2017.
Steiner, C. M., Kickmeier-Rust, M. D., & Albert, D. LEA in private, “ A privacy and data protection framework for a learning analytics toolbox, ” Journal of Learning Analytics, Vol:3, pp. 66-90. 2016.
Mantelero, A., “Personal data for decisional purposes in the age of analytics: From an individual to a collective dimension of data protection.” Computer law & security review, Vol:32, pp.238-255. 2016
Lee, I., “Big data: Dimensions, evolution, impacts, and challenges.” Business Horizons, Vol:60, pp.293-303, 2017.