  Deep learning has proven to be successful in various domains and for
different tasks. However, when it comes to private data several restrictions
are making it difficult to use deep learning approaches in these application
fields. Recent approaches try to generate data privately instead of applying a
privacy-preserving mechanism directly, on top of the classifier. The solution
is to create public data from private data in a manner that preserves the
privacy of the data. In this work, two very prominent GAN-based architectures
were evaluated in the context of private time series classification. In
contrast to previous work, mostly limited to the image domain, the scope of
this benchmark was the time series domain. The experiments show that especially
GSWGAN performs well across a variety of public datasets outperforming the
competitor DPWGAN. An analysis of the generated datasets further validates the
superiority of GSWGAN in the context of time series generation.
