PrivateMine: Inherent Data Protection for Distributed Event Sources

• Applicant: Professor Dr. Florian Tschorsch
• Subject Area: Security and Dependability, Operating-, Communication- and Distributed Systems
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 496119880

In PrivateMine, we consider the shift towards distributed event sources as an opportunity to inherently integrate privacy mechanisms in sourced process mining in general and develop distributed process mining algorithms with provable privacy guarantees in particular. To this end, we follow a data minimization approach as our main design principle and combine two building blocks: provable privacy guarantees such as local differential privacy with probabilistic data structures such as probabilistic counting. Since many process mining algorithms build upon simple statistical functions, e.g., counting the number of directly-follows relations, we argue that this combination provides a promising basis. The main challenge consists of distributing the analytics algorithm itself in a privacy-preserving way. The synergies of SOURCED enable the development of novel privacy-preserving process mining algorithms for distributed event data. The research unit brings together expertise from the fields of process management, data and software engineering, distributed systems, and privacy mechanisms. In particular, the collaboration on resource-efficient distributed process mining and privacy-aware abstraction of event data for distributed process mining are instrumental. With PrivateMine, we therefore provide a substantial research contribution by developing the foundations for the next generation of process mining techniques.

• DFG Programme: Research Units
• Subproject of: FOR 5495: SOURCED – Process Mining on Distributed Event Sources