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Responsible Process Mining (RPM) – Confidentiality

April 26th, 2019 | by

This post is by Majid Rafiei, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.

To gain novel and valuable insights into the actual processes executed within a company, process mining provides a variety of powerful data-driven analyses techniques ranging from automatically discovering process models to detecting and predicting bottlenecks, and process deviations.

On the one hand, recent breakthroughs in process mining resulted in powerful techniques, encouraging organizations and business owners to improve their processes through process mining. On the other hand, there are great concerns about the use of highly sensitive event data. Within an organization, it often suffices that analysts only see the aggregated process mining results without being able to inspect individual cases, events, and persons. When analysis is outsourced also the results need to be encrypted to avoid confidentiality problems.

Surprisingly, little research has been done toward security methods and encryption techniques for process mining. The PADS team presented a novel approach that allows us to hide confidential information in a controlled manner while ensuring that the desired process mining results can still be obtained. In the following you can find a simplified version of our proposed framework.

The paper which introduces this framework, and the connector method to preserve the sequence of activities securely has been selected as the best paper by SIMPDA 2018 ( In this paper, we provide a sample solution for process discovery based on the above-mentioned framework.

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