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.

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