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

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.

SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining

“…Furthermore, a process mining-specific extension of k-anonymity, called TLKC, was introduced in [28]. Previous work focused on improving the utility of these techniques through feature learning-based distance metrics [29]. Another group-based approach was introduced by Batista et al [30], based on the uniformization of events within a group of individuals.…”