Optimizing Privacy-Utility Trade-Off in Healthcare Processes: Simulation, Anonymization, and Evaluation (Using Process Mining) of Event Logs

In healthcare, big data analytics involve balancing patients’ privacy and data utility. Optimizing healthcare data utility often includes limited access to sensitive data by trusted onsite entities. This potentially hinders broader-scale data utilization by third-party data analysts. As a solution, this research simulates a healthcare process-based event log, inspired by a local hospital’s radiology department. The simulated event log is anonymized using k-anonymity. The anonymized and un-anonymized event logs are evaluated, through process discovery techniques, using the process mining tool, ProM 6.11, for Privacy-utility trade-off assessment. Results indicate successful privacy preservation with a distinct loss in utility in the anonymized healthcare process model, which was not visible otherwise. Therefore, to ensure the efficacy of healthcare process analysis on anonymized sensitive event logs, the utilization of process mining techniques is beneficial for process utility and privacy protection evaluation.