An Assessment of Data Location Vulnerability for Human Factors Using Linear Regression and Collaborative Filtering

Abstract

End-user devices and applications (data locations) are becoming more capable and user friendly and are used in various Health Information Systems (HIS) by employees of many health organizations to perform their day to day tasks. Data locations are connected via the internet. The locations have relatively good information security mechanisms to minimize attacks on and through them in terms of technology. However, human factors are often ignored in their security echo system. In this paper, we propose a human factor framework merged with an existing technological framework. We also explore how human factors affect data locations via linear regression computations and rank data location vulnerability using collaborative filtering. Our results show that human factors play a major role in data location breaches. Laptops are ranked as the most susceptible location and electronic medical records as the least. We validate the ranking by root mean square error.

Keywords

• collaborative filtering
• data locations
• HIPAA
• human factors
• information security