This thesis pays attention to the scenarios where the user data are distributed among multiple parties (e.g., companies and institutes). Each party possesses a different set of attributes of the same individuals. The parties aim to collaborate with others to publish a private joint dataset for better decision-making or data-driven analysis. Motivated by the challenge of prohibition of illegal collection of private data, this thesis intends to provide a novel deep-learning-based solution for the privacy-preserving publication of vertically partitioned data. More specifically, this study proposes a so-called Vertically Distributed General Adversarial Network (VDGAN) framework, which would be trained jointly among multiple parties and generate synthetic joint data to replace the integrated real one for data mining tasks. What’s more, two Differential Privacy (DP) protocols are introduced to the framework to provide strong privacy guarantees. Extensive experiments are conducted to evaluate the proposed framework in terms of data utility and privacy preservability with the help of four public datasets with different attribute domains. The results present an apparent improvement compared with the state-of-the-art tree-based models, while some limitations for practical applications should also be considered.