Differentially Private Inductive Miner
<|reference_start|>Differentially Private Inductive Miner: Protecting personal data about individuals, such as event traces in process mining, is an inherently difficult task since an event trace leaks information about the path in a process model that an individual has triggered. Yet, prior anonymization methods of event traces like k-anonymity or event log sanitization struggled to protect against such leakage, in particular against adversaries with sufficient background knowledge. In this work, we provide a method that tackles the challenge of summarizing sensitive event traces by learning the underlying process tree in a privacy-preserving manner. We prove via the so-called Differential Privacy (DP) property that from the resulting summaries no useful inference can be drawn about any personal data in an event trace. On the technical side, we introduce a differentially private approximation (DPIM) of the Inductive Miner. Experimentally, we compare our DPIM with the Inductive Miner on 14 real-world event traces by evaluating well-known metrics: fitness, precision, simplicity, and generalization. The experiments show that our DPIM not only protects personal data but also generates faithful process trees that exhibit little utility loss above the Inductive Miner.<|reference_end|>
@article{schulze2024differentially,
    title={Differentially Private Inductive Miner},
    author={Max Schulze, Yorck Zisgen, Moritz Kirschte, Esfandiar Mohammadi, Agnes
  Koschmider},
    journal={arXiv preprint arXiv:2407.04595},
    year={2024},
    doi={10.1109/ICPM63005.2024.10680684},
    archivePrefix={arXiv},
    eprint={2407.04595},
    primaryClass={cs.CR cs.DB}
}