Towards Privacy-Preserving Process Mining in Healthcare

Process mining has been successfully applied in the healthcare domain and helped to uncover various insights for improving healthcare processes. While benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible use of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of privacy of such data. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Although, until recently, data privacy issues did not get much attention in the process mining community, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data. In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We also propose a framework for privacy-preserving process mining that can support healthcare process mining analyses.