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Business Intelligence - ITS-531

Privacy Issue with Data Mining

Data collected, stored, and analyzed in data mining often contains information about real people with their private information such as identification data(name, address, social security number), demographic data, financial data, income, purchase history, and other personal information. These data are accessible from third-party data providers, but the question is the privacy of the person to whom the data belong (Sharda et al., 2020, Pg. 242). Data mining professionals have ethical and often legal obligations to maintain the privacy of the individual. Information can be disclosed intentionally or unintentionally, posing a threat to the individuals without proper security and protection in all aspects of the computing environment, including the communication environment (Zhang 2018).

One of the ways to maintain individual privacy is to declassify the data prior to performing data mining so that the records cannot be traced back to an individual (Sharda et al., 2020, Pg. 242). It is essential to substantialize user privacy in data mining and maintain the trust of users. Kuang et al. studied the issue of privacy disclosure in location-based services, especially caused through the supplementary information held by attackers. User privacy falls under scrutiny with this kind of vulnerabilities in the applications. They proposed an improved privacy-preserving framework for location-based services based on double cloaking regions with supplementary information constraints. Their studies have shown that their method is effective in solving the strong attack with supplementary information without having to deal with the computational overhead for the client (pg. 12). Researches like this has to be commercially considered for the improved user privacy that prevents infiltration against user data.

References

Kuang, L., Wang, Y., Ma, P., Yu, L., Li, C., Huang, L., Zhu, M. (2017). An Improved Privacy

Preserving Framework for Location-Based Services Based on Double Cloaking Regions

with Supplementary Information Constraints. Security and Communication Networks,

2017. https://doi.org/10.1155/2017/7495974

Sharda, R., Delen, D., Turban, E. (2020). Deep learning. Analytics, data Science, & artificial

intelligence: Systems for decision support (pp. 242). NJ, Pearson.

Zhang, X., Jang-Jaccard, J., Qi, L., Bhuiyan, M., Liu, C. (2018). Privacy Issues in Big Data

Mining Infrastructure, Platforms, and Applications. Security and Communication