# Privacy and Confidentiality in Process Mining -- Threats and Research Challenges

arXiv: https://arxiv.org/abs/2106.00388 · HTML: https://mlsanity.com/paper/2106.00388/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2021.

## Built on by (most cited first, 6 of 6)

- [Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities](https://mlsanity.com/paper/2110.02707/) (2021)
- [Process Mining for Unstructured Data: Challenges and Research Directions](https://mlsanity.com/paper/2401.13677/) (2023)
- [SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining](https://mlsanity.com/paper/2109.08501/) (2021)
- [A Distance Measure for Privacy-preserving Process Mining based on Feature Learning](https://mlsanity.com/paper/2107.06578/) (2021)
- [The Impact of Event Data Partitioning on Privacy-aware Process Discovery](https://mlsanity.com/paper/2507.06008/) (2025)
- [Transforming Football Data into Object-centric Event Logs with Spatial Context Information](https://mlsanity.com/paper/2507.12504/) (2025)

## Abstract (arXiv)

Privacy and confidentiality are very important prerequisites for applying process mining in order to comply with regulations and keep company secrets. This paper provides a foundation for future research on privacy-preserving and confidential process mining techniques. Main threats are identified and related to an motivation application scenario in a hospital context as well as to the current body of work on privacy and confidentiality in process mining. A newly developed conceptual model structures the discussion that existing techniques leave room for improvement. This results in a number of important research challenges that should be addressed by future process mining research.