# Privacy-Preserving Continuous Event Data Publishing

arXiv: https://arxiv.org/abs/2105.11991 · HTML: https://mlsanity.com/paper/2105.11991/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2021.

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

- [Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities](https://mlsanity.com/paper/2110.02707/) (2021)
- [SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining](https://mlsanity.com/paper/2109.08501/) (2021)

## Abstract (arXiv)

Process mining enables organizations to discover and analyze their actual processes using event data. Event data can be extracted from any information system supporting operational processes, e.g., SAP. Whereas the data inside such systems is protected using access control mechanisms, the extracted event data contain sensitive information that needs to be protected. This creates a new risk and a possible inhibitor for applying process mining. Therefore, privacy issues in process mining become increasingly important. Several privacy preservation techniques have been introduced to mitigate possible attacks against static event data published only once. However, to keep the process mining results up-to-date, event data need to be published continuously. For example, a new log is created at the end of each week. In this paper, we elaborate on the attacks which can be launched against continuously publishing anonymized event data by comparing different releases, so-called correspondence attacks. Particularly, we focus on group-based privacy preservation techniques and show that provided privacy requirements can be degraded exploiting correspondence attacks. We apply the continuous event data publishing scenario to existing real-life event logs and report the anonymity indicators before and after launching the attacks.