  In the area of industrial process mining, privacy-preserving event data
publication is becoming increasingly relevant. Consequently, the trade-off
between high data utility and quantifiable privacy poses new challenges.
State-of-the-art research mainly focuses on differentially private trace
variant construction based on prefix expansion methods. However, these
algorithms face several practical limitations such as high computational
complexity, introducing fake variants, removing frequent variants, and a
bounded variant length. In this paper, we introduce a new approach for direct
differentially private trace variant release which uses anonymized
\textit{partition selection} strategies to overcome the aforementioned
restraints. Experimental results on real-life event data show that our
algorithm outperforms state-of-the-art methods in terms of both plain data
utility and result utility preservation.
