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Patient Privacy Violation Detection in Healthcare Critical Infrastructures: An Investigation Using Density-Based Benchmarking

1
Department of Computer Science, Liverpool John Moores University, Liverpool L3 3AF, UK
2
Aintree Hospital, Liverpool L9 7AL, UK
3
Department of Computer Science, University of Sharjah, Sharjah, UAE
*
Author to whom correspondence should be addressed.
Future Internet 2020, 12(6), 100; https://doi.org/10.3390/fi12060100
Received: 5 May 2020 / Revised: 4 June 2020 / Accepted: 5 June 2020 / Published: 8 June 2020
Hospital critical infrastructures have a distinct threat vector, due to (i) a dependence on legacy software; (ii) the vast levels of interconnected medical devices; (iii) the use of multiple bespoke software and that (iv) electronic devices (e.g., laptops and PCs) are often shared by multiple users. In the UK, hospitals are currently upgrading towards the use of electronic patient record (EPR) systems. EPR systems and their data are replacing traditional paper records, providing access to patients’ test results and details of their overall care more efficiently. Paper records are no-longer stored at patients’ bedsides, but instead are accessible via electronic devices for the direct insertion of data. With over 83% of hospitals in the UK moving towards EPRs, access to this healthcare data needs to be monitored proactively for malicious activity. It is paramount that hospitals maintain patient trust and ensure that the information security principles of integrity, availability and confidentiality are upheld when deploying EPR systems. In this paper, an investigation methodology is presented towards the identification of anomalous behaviours within EPR datasets. Many security solutions focus on a perimeter-based approach; however, this approach alone is not enough to guarantee security, as can be seen from the many examples of breaches. Our proposed system can be complementary to existing security perimeter solutions. The system outlined in this research employs an internal-focused methodology for anomaly detection by using the Local Outlier Factor (LOF) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms for benchmarking behaviour, for assisting healthcare data analysts. Out of 90,385 unique IDs, DBSCAN finds 102 anomalies, whereas 358 are detected using LOF. View Full-Text
Keywords: electronic patient record; healthcare critical infrastructures electronic patient record; healthcare critical infrastructures
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Hurst, W.; Boddy, A.; Merabti, M.; Shone, N. Patient Privacy Violation Detection in Healthcare Critical Infrastructures: An Investigation Using Density-Based Benchmarking. Future Internet 2020, 12, 100.

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