Database Decomposition to satisfy the Least Privilege Principle in Healthcare

Authors

DOI:

https://doi.org/10.56394/aris2.v4i1.43

Keywords:

Database Decomposition, Decomposition, Least privilege principles, Impact minimization, Cyber-Security

Abstract

The Multilevel Database Decomposition Framework is a cybersecurity strategy to enhance system robustness and minimize the impact of data breaches with a focus on healthcare systems. With respect to more conventional normalization methods, the framework prioritizes robustness against cyber threats over mere data redundancy reduction. The key strategy of the framework is the decomposition of a database into smaller databases to restrict user access and mitigate the impact of successful intrusions by satisfying the least privilege principle in a more complete way. For this purpose, each database the decomposition produces is uniquely associated with a set of users and the decomposition ensures that each user can access all and only the data his/her operations need. This limits the potential impact of threat agents impersonating users to the information a compromised user can access.

To prevent the propagation of an intrusion across the databases it produces, the framework can apply alternative allocation strategies by distributing the databases to distinct virtual or physical entities according to the security requirement of the original application. This flexibility in allocation management ultimately reinforces defenses against evolving cyber threats and it is the main advantage of the deposition.

As a counterpart of better robustness, some tables will be replicated across the databases the decomposition returns and updates of these tables should be properly replicated to prevent inconsistencies among copies of the same table in distinct databases. The paper includes a performance analysis to evaluate the overheads associated with the alternative allocations. This offers insights into the framework implementation and adaptability to distinct security needs and to evaluate the framework effectiveness for healthcare data systems.

References

A. Brahma and S. Panigrahi. “Application of soft computing techniques in database intrusion detection”. In: Intelligent Technologies: Concepts, Applications, and Future Directions. Springer, 2022, pp. 201–221. DOI: https://doi.org/10.1007/978-981-19-1021-0_9

A. Cuzzocrea and H. Shahriar. “Data masking techniques for NoSQL database security: A systematic review”. In: 2017 IEEE International Conference on Big Data (Big Data). 2017, pp. 4467–4473. DOI: https://doi.org/10.1109/BigData.2017.8258486

European Union. Data protection in the EU. 2023. url:https://ec.europa.eu/info/law/law-topic/data-protection_en.

E. Fern´andez-Medina and M. Piattini. “Designing secure databases”. In: Information and Software Technology 47 (2005), pp. 463–477. DOI: https://doi.org/10.1016/j.infsof.2004.09.013

Downloads

Published

2024-04-15

How to Cite

[1]
V. Sammartino and F. Baiardi, “Database Decomposition to satisfy the Least Privilege Principle in Healthcare”, ARIS2-Journal, vol. 4, no. 1, pp. 47–69, Apr. 2024.