A Distance Measure for Privacy-preserving Process Mining based on Feature Learning

TL;DR

This paper introduces a novel privacy-preserving process mining method that uses feature learning-based event embeddings to measure trace similarity, resulting in anonymized logs with higher utility for analysis.

Contribution

It proposes a new distance measure based on event embeddings to improve anonymization quality by considering activity semantics.

Findings

Embeddings enable meaningful trace distance measurement.

The proposed method preserves more log utility.

Experiments show improved anonymization outcomes.

Abstract

To enable process analysis based on an event log without compromising the privacy of individuals involved in process execution, a log may be anonymized. Such anonymization strives to transform a log so that it satisfies provable privacy guarantees, while largely maintaining its utility for process analysis. Existing techniques perform anonymization using simple, syntactic measures to identify suitable transformation operations. This way, the semantics of the activities referenced by the events in a trace are neglected, potentially leading to transformations in which events of unrelated activities are merged. To avoid this and incorporate the semantics of activities during anonymization, we propose to instead incorporate a distance measure based on feature learning. Specifically, we show how embeddings of events enable the definition of a distance measure for traces to guide event log anonymization. Our experiments with real-world data indicate that anonymization using this measure, compared to a syntactic one, yields logs that are closer to the original log in various dimensions and, hence, have higher utility for process analysis.