Privacy based data mining

Privacy based data mining

Privacy based data mining is important for sectors like Healthcare, Pharmaceuticals, Research, and Security Service Providers. There is a high possibility of handling big data that is unsafe because of the failure to use the various security mechanisms like firewalls because the cybersecurity techniques cannot be practical to large amounts of data. Analyzing data through data mining would also involve access to insecure web interfaces, vulnerable network servers, and weak transport encryption, which would cause the data collection and integration mechanisms to lose sensitive data.

Data mining involves the analysis of data from diverse data sources, much of which would include confidential data. If the cybersecurity mechanisms that support the safety of the analytics systems do not guarantee quality security against data breaches, privacy issues such as data disclosure, leakage of medical data, or unnecessary data breaches will occur. Attackers
can use data mining methods and procedures to find out sensitive data and release it to the public, and thus data breach happens (Abouelmehdi, Beni-Hessane, & Khaloufi, 2018). The privacy of most data mining models is not quality enough to protect personal data from possible data breach.

The generic solutions developed to ensure quality data privacy for data mining techniques are insufficient and ineffective. Attribute removal and aggregation of numeric values do not have the required efficiency in providing privacy to the data supplied to data mining processes for data analysis. The various operations of data mining are not provided with the required privacy measures because of insufficiency in the available solutions for ensuring data security to all the data security platforms. Still, data utility and information loss is trade-offs when active data mining is conducted concerning privacy measures (Shah, & Gulati, 2016).

References

Zhang, X., Jang-Jaccard, J., Qi, L., Bhuiyan, M. Z., & Liu, C. (2018). Privacy Issues in Big Data Mining Infrastructure, Platforms, and Applications. Security and Communication Networks, 2018.

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

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

Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: preserving security and privacy. Journal of Big Data, 5(1), 1.

Moura, J., & Serrão, C. (2019). Security and privacy issues of big data. In Cloud Security: Concepts, Methodologies, Tools, and Applications (pp. 1598-1630). IGI Global.