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Cyber-Analytics: Identifying Discriminants of Data Breaches

机译:网络分析:识别数据泄露的区别

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摘要

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.
机译:在这项研究中,考虑了数据泄露特征与受这些违反影响的个人数量之间的关系。从卫生和公共服务部的违规报告数据库中获取数据,并使用SPSS进行分析。回归分析显示,黑客/ IT事件违规类型和网络服务器违规位置是受影响个人数量的最重要预测指标。但是,将它们结合起来并不能预测。此外,结合使用时,网络服务器位置和未授权访问/泄露违规类型是可预测的。进一步的方差分析表明,所涵盖实体的类型和业务伙伴的存在是重要的预测指标,而发生违规事件的地理区域则微不足道。这项研究的结果揭示了医疗保健违规特征与受影响个人数量之间的多种关联,这表明更多的个人受到黑客/ IT事件和网络服务器违规的影响,并且网络服务器违规位置和未授权访问/披露违规类型是可预测的结合。

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