Abstract: Before synthetic data (SD) generators are able to generate entire electronic health records, many challenges still have to be tackled. One of these challenges is to generate both privacy-preserving and longitudinal SD. This research combines the research streams of longitudinal SD and privacy-preserving static SD and presents a novel GAN architecture called Time-ADS-GAN. Time-ADS-GAN outperforms current state-of-the-art models on both utility and privacy on three datasets and is able to reproduce the results of a healthcare study significantly better than TimeGAN. As a second contribution, a variation of the $\epsilon$-identifiability metric is introduced and used in the analysis.