Cyber-Analytics: Identifying Discriminants of Data Breaches
Date
2019-06
Authors
Dolezel, Diane
McLeod, Alexander
Journal Title
Journal ISSN
Volume Title
Publisher
American Health Information Management Association
Abstract
In this study, the relationship between data breach characteristics and the number of individuals affected by these violations was considered. Data were acquired from the Department of Health and Human Services breach reporting database and analyzed using SPSS. Regression analyses revealed that the hacking/IT incident breach type and network server breach location were the most significant predictors of the number of individuals affected; however, they were not predictive when combined. Moreover, network server location and unauthorized access/disclosure breach type were predictive when combined. Additional analyses of variance revealed that covered entity type and business associate presence were significant predictors, while the geographic region of a breach occurrence was insignificant. The results of this study revealed several associations between healthcare breach characteristics and the number of individuals affected, suggesting that more individuals are affected in hacking/IT incidents and network server breaches independently and that network server breach location and unauthorized access/disclosure breach type were predictive in combination.
Description
Keywords
security, protected health information, breach portal, security modeling, cyber-analytics, data breach, Health Information Management
Citation
Dolezel, D. M., & McLeod, A. (2019). Cyber-analytics: Identifying discriminants of data breaches. Perspectives in Health Information Management, 16(1a), pp. 1–17.