Motiwalla Luvai, Li Xiao-Bai
Department of Operations and Information Systems Manning School of Business University of Massachusetts Lowell Lowell, MA 01854
Int J Bus Inf Syst. 2013 Jan 1;13(2). doi: 10.1504/IJBIS.2013.054335.
The extensive use of electronic health data has increased privacy concerns. While most healthcare organizations are conscientious in protecting their data in their databases, very few organizations take enough precautions to protect data that is shared with third party organizations. Recently the regulatory environment has tightened the laws to enforce privacy protection. The goal of this research is to explore the application of data masking solutions for protecting patient privacy when data is shared with external organizations for research, analysis and other similar purposes. Specifically, this research project develops a system that protects data without removing sensitive attributes. Our application allows high quality data analysis with the masked data. Dataset-level properties and statistics remain approximately the same after data masking; however, individual record-level values are altered to prevent privacy disclosure. A pilot evaluation study on large real-world healthcare data shows the effectiveness of our solution in privacy protection.
电子健康数据的广泛使用增加了对隐私的担忧。虽然大多数医疗保健组织在保护其数据库中的数据方面尽职尽责,但很少有组织采取足够的预防措施来保护与第三方组织共享的数据。最近,监管环境收紧了法律以加强隐私保护。本研究的目的是探索数据掩码解决方案在数据与外部组织共享以进行研究、分析和其他类似目的时保护患者隐私的应用。具体而言,本研究项目开发了一个在不删除敏感属性的情况下保护数据的系统。我们的应用程序允许对掩码数据进行高质量的数据分析。数据掩码后数据集级别的属性和统计数据大致保持不变;然而,单个记录级别的值会被更改以防止隐私泄露。一项针对大型真实世界医疗保健数据的试点评估研究表明了我们的解决方案在隐私保护方面的有效性。