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March 12, 2026Open Access

Towards Measuring Privacy in Distributed Process Mining

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Authors

MWMaximilian WeisenseelFTFlorian Tschorsch

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Overview

This research discusses privacy in distributed process mining, addressing challenges in data processing across organizations.

Key Points

  • The research aims to explore privacy issues in distributed process mining and propose metrics for measurement.
  • Introduced two use cases for distributed process mining.
  • Defined an adversary model to assess privacy risks.
  • Discussed guarantees for privacy independent of data.
  • Identified challenges of processing data without a secure, trusted location.
  • Proposed metrics to evaluate privacy in real-world applications.
  • Highlighted the importance of privacy when organizations collaborate.

Cite This Study

Weisenseel et al. (2026) studied this question.

synapsesocial.com/papers/69b25b7196eeacc4fceca27fhttps://doi.org/10.18420/emisa2026_03
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  1. 1Process mining on distributed data sources2025
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  3. 3EdgeMiner: distributed process mining at the data sources2025
  4. 4Decentralized Process Learning for Cyber-Physical Systems2026
  5. 5DPM-Bench: Benchmark for distributed process mining algorithms on cyber-physical systems2025