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.

翻译：深度学习已在多个领域及不同任务中展现出成功应用。然而，当涉及私有数据时，诸多限制使得深度学习在这些应用领域难以施展。近年来的研究尝试以私有方式生成数据，而非直接在分类器之上应用隐私保护机制。其解决方案是通过隐私保护方式从私有数据中创建公开数据。本研究在私有时间序列分类背景下，评估了两种极具代表性的基于GAN的架构。与以往主要局限于图像领域的研究不同，本基准测试聚焦于时间序列领域。实验表明，GSWGAN在多个公开数据集上表现优异，显著优于其竞争对手DPWGAN。对生成数据集的进一步分析也印证了GSWGAN在时间序列生成方面的卓越性能。