A Risk-Based Pseudonymization Framework for Healthcare Big Data: A Korean Perspective

The utilization of healthcare big data is rapidly increasing worldwide, but privacy concerns often limit its use, particularly for sensitive information.This study proposes a new pseudonymization methodology that considers data disclosure environments to overcome these limitations.The proposed framework categorizes data and disclosure risks into three levels: low risk for secure internal environments, moderate risk for controlled but semi-open environments, and high risk for public or external environments.These levels provide pseudonymization standards that enable the safe and efficient use of sensitive information.To validate the methodology, a focus group interview and survey were conducted with 30 healthcare experts.Results showed high validity (average score 4.29) and effectiveness (average score 4.63) of the proposed framework.This approach could significantly enhance the utilization of healthcare big data while maintaining privacy protection.