Bachelor Thesis - Implementation of Privacy Preservation Techniques in Process Mining
Recent years have seen tremendous advances in process mining which are reflected by the growing number of commercial process mining tools available today. There are over 25 commercial products supporting process mining (Celonis, Disco, Minit, myInvenio, ProcessGold, QPR, etc.). All support process discovery and can be used to improve compliance and performance issues. All the process mining activities are performed on the basis of even data, which may include direct and indirect sensitive information.
Responsible Data Science (RDS) is a set of technical algorithms and social laws for ensuring Fairness, Accuracy, Confidentiality, and Transparency (FACT) during the whole pipeline of Data Science. In this project, the focus is on confidentiality/privacy in process mining (how to answer questions without revealing secrets?). The goal is to implement/integrate the state-of-the-art privacy preservation techniques in a web-based tool with an easy-to-use interface for users. The web-based tool is PPDP-PM implemented by the Chair of Process and Data Science (PADS).
- Data Science in Action
- Responsible Data Science: Using Event Data in a “People Friendly” Manner
- Practical Aspect of Privacy-Preserving Data Publishing in Process Mining
- PPDP-PM: Privacy-Preserving Data Publishing in Process Mining
Prof.dr.ir. Wil van der Aalst
For more information
Contact Majid Rafiei. Make sure to include detailed information about your background and scores for related courses.