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A Hybrid Density-Based Outlier Detection Model for Privacy in Electronic Patient Records

Boddy, A, Hurst, W, MacKay, M and El Rhalibi, A A Hybrid Density-Based Outlier Detection Model for Privacy in Electronic Patient Records. In: ICIM 2019 Conference Proceedings . (The 5th IEEE International Conference on Information Management (ICIM2019), 24 March 2019 - 28 March 2019, Cambridge). (Accepted)

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Abstract

This research concerns the detection of unauthorised access within hospital networks through the real-time analysis of audit logs. Privacy is a primary concern amongst patients due to the rising adoption of Electronic Patient Record (EPR) systems. There is growing evidence to suggest that patients may withhold information from healthcare providers due to lack of Trust in the security of EPRs. Yet, patient record data must be available to healthcare providers at the point of care. Ensuring privacy and confidentiality of that data is challenging. Roles within healthcare organisations are dynamic and relying on access control is not sufficient. Through proactive monitoring of audit logs, unauthorised accesses can be detected and presented to an analyst for review. Advanced data analytics and visualisation techniques can be used to aid the analysis of big data within EPR audit logs to identify and highlight pertinent data points. Employing a human-in-the-loop model ensures that suspicious activity is appropriately investigated and the data analytics is continuously improving. This paper presents a system that employs a Human-in-the-Loop Machine Learning (HILML) algorithm, in addition to a density-based local outlier detection model. The system is able to detect 145 anomalous behaviours in an unlabelled dataset of 1,007,727 audit logs. This equates to 0.014% of the EPR accesses being labelled as anomalous in a specialist Liverpool (UK) hospital.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions: Computer Science
Date Deposited: 29 Jan 2019 11:41
Last Modified: 29 Jan 2019 11:41
URI: http://researchonline.ljmu.ac.uk/id/eprint/10036

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