UC De Identification Processes Before the Analysis of Data Responses

I’m working on a computer science writing question and need an explanation to help me understand better.

Answer 1:

Chapter 4

Data mining is a real-world setting that deals with data about a real individual. This information or data is in the form of names, addresses, social security numbers, driver’s license numbers, ages, sex, financial data such as income, bank balance, credit card limits, investment accounts. This data is very personal to the individual, and most individuals would not want this data disclosed (Chamikara et al., 2020). When this data gets into the wrong hands, i.e., hackers and fraudsters can lead to identity theft and illegal criminal activities using a certain individual’s name. This, in turn, can cause much damage to the affected people, and they may sue the company responsible for this personal information breach. This will also cause huge losses to the company to file the lawsuit and compensate these individuals for violating their privacy rights (Chamikara et al., 2020).

The data analyst needs to prevent such violations of the privacy rights of individuals. A brief example that can support the statement mentioned above is the Equifax data breach. Equifax had to compensate many individuals with millions of dollars, which could have been easily avoided (Shah & Gulati, 2016). To overcome this issue of putting people’s private information at risk, most sensible and responsible firms incorporate data de-identification before data analysis. Most open-source databases ask users to content that the user will try to identify the individual’s identity behind the listed numbers under no circumstances.

Hence it is substantiated that the privacy of an individual’s data used for analytics purposes is kept private at all cost and keeps the data safe from misuse (Shah & Gulati, 2016).

References

Chamikara, M. A. P., Bertok, P., Liu, D., Camtepe, S., & Khalil, I. (2020). Efficient privacy preservation of big data for accurate data mining. Information Sciences, 527, 420-443.

Shah, A., & Gulati, R. (2016). Privacy preserving data mining: Techniques classification and implications—A survey. Int. J. Comput. Appl, 137(12), 40-46.

Answer 2:

Privacy and Security Issues in Data Mining

Technological advancement is an opportunity to enhance successful business operations by using technology like data mining. However, data mining is associated with increased privacy and security issues, which affect the company’s integrity while interacting with the customer’s data. Research by Mendes and Vilela (2017) acknowledged that security is a major issue in data mining, which affects the opportunity to enhance effective decision-making. Customers cannot trust the company with its data due to the privacy challenges due to insecurity. The company can also suffer from a damaged reputation.

Companies have gradually advanced, and they are collecting the customer’s data using data-mining strategies. Some companies are sharing the information with third parties without acknowledging the customers. Sharda et al. (2020) discussed a privacy issue by JetBlue Airlines, where whereby the company-provided the customer’s data to Torch Concepts, a US government contractor. It is a perfect example of how data mining has privacy issues affecting business efficiency. The companies do not play their roles and responsibility to protect the customers’ information. In many cases, the company does not acknowledge the customers about the data collection processes. Companies are those using customers’ data in advertisements without acknowledging the customers, which increases privacy issues.

Data mining privacy issues are substantiated since technological advancement is giving organizations more opportunities to collect the data. For example, technological evolution has increased the use of mobile technology and the Internet of Things (IoT), which increases the framework for organizations to collect the customer’s data. Some customers may not be aware of the company’s intention, which results in security and privacy problems. As a result, the privacy issues are substantiated, and they affect the business efficiency and how an organization can maximize the performance.

References

Mendes, R., & Vilela, J. P. (2017). Privacy-preserving data mining: methods, metrics, and applications. IEEE Access, 5, 10562-10582.

Sharda, R., Delen, D., & Turban, E. (2020). Analytics, data science, & artificial intelligence: Systems for decision support. Hoboken, N. J.: Pearson.