Group-based privacy preservation techniques for process mining

Abstract Process mining techniques help to improve processes using event data. Such data are widely available in information systems. However, they often contain highly sensitive information. For example, healthcare systems record that can be utilized by process the treatment process, reduce patient’s waiting times, resource productivity, etc. recorded include related activities. Responsible should provide insights about underlying processes, yet, at same time, it not reveal In this paper, we discuss challenges regarding directly applying existing well-known group-based privacy preservation techniques, e.g., k -anonymity, l -diversity, etc, We formal definitions of attack models and introduce an effective technique for mining. Our covers main perspectives including control-flow, case, organizational perspectives. The proposed provides interpretable adjustable parameters handle different aspects. employ real-life evaluate both utility result show effectiveness technique. also compare approach with other approaches privacy-preserving publishing.