Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/335074 
Year of Publication: 
2025
Citation: 
[Journal:] Process Science [ISSN:] 2948-2178 [Volume:] 2 [Issue:] 1 [Article No.:] 28 [Publisher:] Springer International Publishing [Place:] Cham [Year:] 2025
Publisher: 
Springer International Publishing, Cham
Abstract: 
The increasing reliance on data-driven technologies such as Artificial Intelligence and Process Mining has transformed various sectors. Yet, access to real-world data is often restricted by privacy concerns. Synthetic data offers a promising solution by enabling secure data sharing while preserving key characteristics for analysis. This paper introduces the Private Autoregressive Log Synthesizer (PALSYN), a novel approach for generating synthetic event logs with differential privacy guarantees. It employs advanced deep learning techniques to capture the complexity of event data while ensuring privacy. In contrast to existing methods, PALSYN can synthesize private multi-perspective event logs. The evaluation demonstrates the approach’s ability to generate synthetic event logs that closely resemble the original data across key metrics. However, the results highlight the inherent privacy-utility trade-off, with stricter privacy settings introducing noise that strongly impacts utility. By enabling the generation of synthetic event logs with formal privacy guarantees, PALSYN demonstrates significant potential for securely sharing event data in privacy-sensitive domains.
Subjects: 
Synthetic event logs
Process mining
Differential privacy
Deep learning
Generative models
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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