A Privacy-Preserving Approach to Conformance Checking
For process mining practitioners needing to check conformance without exposing sensitive process models or event logs, this work provides a secure but computationally expensive solution.
The paper proposes a privacy-preserving conformance checking method using homomorphic encryption and string processing, enabling secure alignment computation without revealing the process model or event log to either party. Evaluation on synthetic and real-world logs shows feasibility but with high memory and processing costs.
Conformance checking, one of the main process mining operations, aims to identify discrepancies between a process model and an event log. The model represents the expected behaviour, whereas the event log represents the actual process behaviour as captured in information systems records. Traditionally, the process model and the event log are both accessible to the business analyst performing the conformance checking. However, in some contexts, it is necessary to keep either the model or the log private to protect critical or sensitive information. In this paper, we propose a secure approach to conformance checking based on string processing algorithms and homomorphic encryption, where the process model and event log ar not visible to either the model's or event log's owner. The proposed technique is based on alignments, a well-known formalism used for conformance checking. An evaluation is performed using a synthetic and a real-world event log, showing that conformance checking can be securely computed at the expense of high memory and processing requirements.