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What are the privacy issues with data mining? Do you think they are substantiated?

Discussion # 1:

Data mining processes often involve looking into people’s behavior based on past purchases, routine travel, or events in their lives to identify trends and patterns. The practice raises ethical issues for organizations that mine the data and privacy concerns for consumers (Turner, 2020). There have been several incidents of data breaches by big companies over the past years concerning data mining. One such case is an incident involving Facebook and data miner Cambridge Analytica (Schneble et al., 2018).

Businesses should have a clear privacy policy and terms of service about how they will use and share their customers’ information. They should also use de-identification of the customer records before applying data mining applications so that the records cannot be traced to an individual (Sharda et al.,2019). Since large information collected for data mining can be a tempting target for hackers or cybercriminals, businesses should ensure that they have high-security methods implemented to prevent any data breaches.

Several privacy laws have been enforced to legally obliged businesses to maintain and protect consumers’ privacy rights. One such law is CCPA (California Consumer Privacy Law), which was enforced on Jan 1, 2020. This law gives privacy rights to California residents as they can ask businesses to provide them the personal information that the business has collected about them and how they are using it. They also have the right to ask businesses to delete personal information collected about them and opt-out to sell their personal information (Becerra, 2018). Another such law is Europe’s data privacy and security law, i.e., General Data Protection Regulation (GDPR), which includes hundreds of pages’ worth of requirements for organizations worldwide (Wolford, 2018).


Becerra, X. (2018). California Consumer Privacy Act (CCPA). Retrieved from State of California Department of Justice:

Schneble, C. O., Elger, B. S., & Shaw, D. (2018, July 2). The Cambridge Analytica affair and Internet-mediated research. Retrieved from Embo Reports:

Sharda, R., Delen, D., & Turban, E. (2019). Business Intelligence and Analytics. Pearson.

Turner, T. ( 2020, July 17). Data Mining. Retrieved from

Wolford, B. (2018). What is GDPR, the EU’s new data protection law? Retrieved from

Discussion # 2:

What are the privacy issues with data mining? Do you think they are substantiated?

Data mining is a cycle that utilizes factual, numerical, and artificial intelligence procedures to separate and distinguish helpful data and resulting information from enormous arrangements of data. These patterns can be as business rules, affinities, connections, trends, or forecast models.

Information that are gathered, put away, and investigated in data mining regularly contain data about genuine people. Such data can incorporate ID information, segment information, monetary information, buying history, and other individual information. The vast majority of these information can be gotten from some third-party information suppliers. There have been various examples in the new past when organizations shared their client information with others without looking for the unequivocal assent of their clients. For example, as the majority of you may review, in 2003, JetBlue Airlines gave more than 1 million traveler records of clients to Torch Concepts, a U.S. government project worker. Light at that point hence expanded the traveler information with extra data, for example, family sizes and Social Security Numbers data bought from the information specialist Acxiom. The combined individual data set was planned to be utilized for an information mining task to create potential fear monger profiles. The entirety of this was managed without warning or assent of travelers. At the point when information on the exercises got out, in any case, many security claims were recorded against JetBlue, Torch, and Acxiom, and a few U.S. congresspersons required an examination concerning the episode (Wald, 2004).

The principle question here is the protection of the individual to whom the information belonged to have. To keep up the privacy and security of people’s privileges, data mining experts have moral (and regularly legitimate) commitments. One approach to achieve this is the cycle of de-Identify of the client records preceding to apply data mining applications with the goal that the records cannot be followed to any individual person. Numerous openly accessible information sources (e.g., CDC information, SEER information, UNOS information) are as of now de-distinguished. Preceding getting to these information sources, clients are regularly approached to assent that by no means will they attempt to recognize the people behind those figures.


Sharda, R., Delen, D., & Turban, E. (2019). Analytics, data science, & artificial intelligence: Systems for decision support (11th ed.). Hoboken, N. J.: Pearson.
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