  Privacy-preserving process mining enables the analysis of business processes
using event logs, while giving guarantees on the protection of sensitive
information on process stakeholders. To this end, existing approaches add noise
to the results of queries that extract properties of an event log, such as the
frequency distribution of trace variants, for analysis.Noise insertion neglects
the semantics of the process, though, and may generate traces not present in
the original log. This is problematic. It lowers the utility of the published
data and makes noise easily identifiable, as some traces will violate
well-known semantic constraints.In this paper, we therefore argue for privacy
preservation that incorporates a process semantics. For common trace-variant
queries, we show how, based on the exponential mechanism, semantic constraints
are incorporated to ensure differential privacy of the query result.
Experiments demonstrate that our semantics-aware anonymization yields event
logs of significantly higher utility than existing approaches.
