# Control-flow Reconstruction Attacks on Business Process Models

arXiv: https://arxiv.org/abs/2409.10986 · HTML: https://mlsanity.com/paper/2409.10986/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [A Survey of Privacy Attacks in Machine Learning](https://mlsanity.com/paper/2007.07646/) (2020)
- [Reconstruction and Membership Inference Attacks against Generative Models](https://mlsanity.com/paper/1906.03006/) (2019)
- [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://mlsanity.com/paper/1705.02288/) (2017)
- [Automated Discovery of Business Process Simulation Models from Event Logs](https://mlsanity.com/paper/1910.05404/) (2019)
- [Group-Based Privacy Preservation Techniques for Process Mining](https://mlsanity.com/paper/2105.11983/) (2021)
- [Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models](https://mlsanity.com/paper/2303.17463/) (2023)
- [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)
- [TraVaG: Differentially Private Trace Variant Generation Using GANs](https://mlsanity.com/paper/2303.16704/) (2023)
- [Privacy-Preserving Directly-Follows Graphs: Balancing Risk and Utility in Process Mining](https://mlsanity.com/paper/2012.01119/) (2020)
- [PMDG: Privacy for Multi-Perspective Process Mining through Data Generalization](https://mlsanity.com/paper/2305.00960/) (2023)

## Built on by (most cited first, 1 of 1)

- [The Impact of Event Data Partitioning on Privacy-aware Process Discovery](https://mlsanity.com/paper/2507.06008/) (2025)

## Abstract (arXiv)

Process models may be automatically generated from event logs that contain as-is data of a business process. While such models generalize over the control-flow of specific, recorded process executions, they are often also annotated with behavioural statistics, such as execution frequencies.Based thereon, once a model is published, certain insights about the original process executions may be reconstructed, so that an external party may extract confidential information about the business process. This work is the first to empirically investigate such reconstruction attempts based on process models. To this end, we propose different play-out strategies that reconstruct the control-flow from process trees, potentially exploiting frequency annotations. To assess the potential success of such reconstruction attacks on process models, and hence the risks imposed by publishing them, we compare the reconstructed process executions with those of the original log for several real-world datasets.