A Privacy-Preserving Approach to Conformance Checking
Luis Rodríguez-Flores, Luciano García-Bañuelos, Abel Armas-Cervantes, Astrid Rivera-Partida
TLDR
This paper introduces a privacy-preserving conformance checking method using homomorphic encryption, securing process models and event logs.
Key contributions
- Develops a privacy-preserving conformance checking method for process mining.
- Employs homomorphic encryption and string processing to secure models and event logs.
- Guarantees data privacy, preventing owners from viewing each other's sensitive information.
- Demonstrates secure computation is possible, albeit with high memory and processing costs.
Why it matters
This paper addresses a critical privacy gap in process mining, enabling organizations to perform conformance checks on sensitive data without compromising confidentiality. It offers a novel solution for industries handling private information, despite current computational overheads.
Original Abstract
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.
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