# SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining

arXiv: https://arxiv.org/abs/2109.08501 · HTML: https://mlsanity.com/paper/2109.08501/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science](https://mlsanity.com/paper/1905.06169/) (2019)
- [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)
- [Privacy-Preserving Directly-Follows Graphs: Balancing Risk and Utility in Process Mining](https://mlsanity.com/paper/2012.01119/) (2020)
- [Privacy-aware Process Performance Indicators: Framework and Release Mechanisms](https://mlsanity.com/paper/2103.11740/) (2021)
- [Privacy-Preserving Continuous Event Data Publishing](https://mlsanity.com/paper/2105.11991/) (2021)
- [A Distance Measure for Privacy-preserving Process Mining based on Feature Learning](https://mlsanity.com/paper/2107.06578/) (2021)

## Built on by (most cited first, 2 of 2)

- [TraVaG: Differentially Private Trace Variant Generation Using GANs](https://mlsanity.com/paper/2303.16704/) (2023)
- [Releasing Differentially Private Event Logs Using Generative Models](https://mlsanity.com/paper/2504.06418/) (2025)

## Abstract (arXiv)

Privacy-preserving process mining enables the analysis of business processes using event logs, while giving guarantees on the protection of sensitive information on process stakeholders. To this end, existing approaches add noise to the results of queries that extract properties of an event log, such as the frequency distribution of trace variants, for analysis.Noise insertion neglects the semantics of the process, though, and may generate traces not present in the original log. This is problematic. It lowers the utility of the published data and makes noise easily identifiable, as some traces will violate well-known semantic constraints.In this paper, we therefore argue for privacy preservation that incorporates a process semantics. For common trace-variant queries, we show how, based on the exponential mechanism, semantic constraints are incorporated to ensure differential privacy of the query result. Experiments demonstrate that our semantics-aware anonymization yields event logs of significantly higher utility than existing approaches.