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显著成效。然而，当涉及私有数据时，诸多限制使得深度学习方法难以应用于这些领域。最新研究倾向于通过生成私有数据的公开版本，而非直接在分类器上施加隐私保护机制。解决方案是以保护数据隐私的方式，从私有数据创建公开数据。本研究针对两种主流的生成对抗网络架构在私有时间序列分类场景下进行了评估。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。实验结果表明，GSWGAN在多个公开数据集上表现优异，其性能优于同类竞品DPWGAN。对生成数据集的进一步分析也证实了GSWGAN在时间序列生成任务中的优越性。