  Process mining is rapidly growing in the industry. Consequently, privacy
concerns regarding sensitive and private information included in event data,
used by process mining algorithms, are becoming increasingly relevant.
State-of-the-art research mainly focuses on providing privacy guarantees, e.g.,
differential privacy, for trace variants that are used by the main process
mining techniques, e.g., process discovery. However, privacy preservation
techniques for releasing trace variants still do not fulfill all the
requirements of industry-scale usage. Moreover, providing privacy guarantees
when there exists a high rate of infrequent trace variants is still a
challenge. In this paper, we introduce TraVaG as a new approach for releasing
differentially private trace variants based on \text{Generative Adversarial
Networks} (GANs) that provides industry-scale benefits and enhances the level
of privacy guarantees when there exists a high ratio of infrequent variants.
Moreover, TraVaG overcomes shortcomings of conventional privacy preservation
techniques such as bounding the length of variants and introducing fake
variants. Experimental results on real-life event data show that our approach
outperforms state-of-the-art techniques in terms of privacy guarantees, plain
data utility preservation, and result utility preservation.
