Abstract
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.