# The Impact of Event Data Partitioning on Privacy-aware Process Discovery

arXiv: https://arxiv.org/abs/2507.06008 · HTML: https://mlsanity.com/paper/2507.06008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science](https://mlsanity.com/paper/1905.06169/) (2019)
- [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://mlsanity.com/paper/1705.02288/) (2017)
- [Group-Based Privacy Preservation Techniques for Process Mining](https://mlsanity.com/paper/2105.11983/) (2021)
- [Privacy and Confidentiality in Process Mining -- Threats and Research Challenges](https://mlsanity.com/paper/2106.00388/) (2021)
- [PRIPEL: Privacy-Preserving Event Log Publishing Including Contextual Information](https://mlsanity.com/paper/2006.12856/) (2020)
- [Secure Multi-Party Computation for Inter-Organizational Process Mining](https://mlsanity.com/paper/1912.01855/) (2019)
- [Quantifying the Re-identification Risk of Event Logs for Process Mining](https://mlsanity.com/paper/2003.10707/) (2020)
- [Towards Quantifying Privacy in Process Mining](https://mlsanity.com/paper/2012.12031/) (2020)
- [Libra: High-Utility Anonymization of Event Logs for Process Mining via Subsampling](https://mlsanity.com/paper/2206.13050/) (2022)
- [TraVaG: Differentially Private Trace Variant Generation Using GANs](https://mlsanity.com/paper/2303.16704/) (2023)
- [Discovering Hierarchical Processes Using Flexible Activity Trees for Event Abstraction](https://mlsanity.com/paper/2010.08302/) (2020)
- [GECCO: Constraint-driven Abstraction of Low-level Event Logs](https://mlsanity.com/paper/2112.01897/) (2021)
- [PMDG: Privacy for Multi-Perspective Process Mining through Data Generalization](https://mlsanity.com/paper/2305.00960/) (2023)
- [Trusted Execution Environment for Decentralized Process Mining](https://mlsanity.com/paper/2312.12105/) (2023)
- [Differentially Private Release of Event Logs for Process Mining](https://mlsanity.com/paper/2201.03010/) (2022)
- [Control-flow Reconstruction Attacks on Business Process Models](https://mlsanity.com/paper/2409.10986/) (2024)

## Abstract (arXiv)

Information systems support the execution of business processes. The event logs of these executions generally contain sensitive information about customers, patients, and employees. The corresponding privacy challenges can be addressed by anonymizing the event logs while still retaining utility for process discovery. However, trading off utility and privacy is difficult: the higher the complexity of event log, the higher the loss of utility by anonymization. In this work, we propose a pipeline that combines anonymization and event data partitioning, where event abstraction is utilized for partitioning. By leveraging event abstraction, event logs can be segmented into multiple parts, allowing each sub-log to be anonymized separately. This pipeline preserves privacy while mitigating the loss of utility. To validate our approach, we study the impact of event partitioning on two anonymization techniques using three real-world event logs and two process discovery techniques. Our results demonstrate that event partitioning can bring improvements in process discovery utility for directly-follows-based anonymization techniques.